From 138583399739caac72e78d7fbe09a7511d437cf3 Mon Sep 17 00:00:00 2001
From: Prathamesh Shakewar <shakewarprathmesh@gmail.com>
Date: Sat, 3 Jan 2026 10:12:44 +0530
Subject: [PATCH] Addgraph.py

Addgraph for  predicted stock prices this method  is  easy to  understand
---
 Addgraph.py | 86 +++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 86 insertions(+)
 create mode 100644 Addgraph.py

diff --git a/Addgraph.py b/Addgraph.py
new file mode 100644
index 0000000..10e5313
--- /dev/null
+++ b/Addgraph.py
@@ -0,0 +1,86 @@
+
+# This program predicts stock prices by using machine learning models
+
+#Install the dependencies
+import matplotlib.pyplot as plt
+import quandl
+import numpy as np 
+from sklearn.linear_model import LinearRegression
+from sklearn.svm import SVR
+from sklearn.model_selection import train_test_split
+
+#Get the stock data
+df = quandl.get("WIKI/AMZN")
+# Take a look at the data
+print(df.head())
+
+# Get the Adjusted Close Price
+df = df[['Adj. Close']]
+#Take a look at the new data
+print(df.head())
+
+# A variable for predicting 'n' days out into the future
+forecast_out = 30 #forecast_out = 'n=30' days
+#Create another column (the target or dependent variable) shifted 'n' units up
+df['Prediction'] = df[['Adj. Close']].shift(-forecast_out)
+#print the new data set
+print(df.tail())
+
+### Create the indeoendent data set (X)  #######
+# Convert the dataframe to a numpy array
+X = np.array(df.drop(['Prediction'],1))
+
+#Remove the last 'n' rows
+X = X[:-forecast_out]
+print(X)
+
+### Create the dependent data set (y)  #####
+# Convert the dataframe to a numpy array (All of the values including the NaN's)
+y = np.array(df['Prediction'])
+# Get all of the y values except the last 'n' rows
+y = y[:-forecast_out]
+print(y)
+
+# Split the data into 80% training and 20% testing
+x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+# Create and train the Support Vector Machine (Regressor)
+svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)
+svr_rbf.fit(x_train, y_train)
+
+# Testing Model: Score returns the coefficient of determination R^2 of the prediction. 
+# The best possible score is 1.0
+svm_confidence = svr_rbf.score(x_test, y_test)
+print("svm confidence: ", svm_confidence)
+
+# Create and train the Linear Regression  Model
+lr = LinearRegression()
+# Train the model
+lr.fit(x_train, y_train)
+
+# Testing Model: Score returns the coefficient of determination R^2 of the prediction. 
+# The best possible score is 1.0
+lr_confidence = lr.score(x_test, y_test)
+print("lr confidence: ", lr_confidence)
+
+# Set x_forecast equal to the last 30 rows of the original data set from Adj. Close column
+x_forecast = np.array(df.drop(['Prediction'],1))[-forecast_out:]
+print(x_forecast)
+
+# Print linear regression model predictions for the next 'n' days
+lr_prediction = lr.predict(x_forecast)
+print(lr_prediction)
+
+# Print support vector regressor model predictions for the next 'n' days
+svm_prediction = svr_rbf.predict(x_forecast)
+print(svm_prediction)
+
+
+plt.figure(figsize=(10,5))
+plt.plot(y_test, color='blue', label='Actual Price')
+plt.plot(predictions, color='red', linestyle='--', label='Predicted Price')
+plt.title('Stock Price Prediction')
+plt.xlabel('Time')
+plt.ylabel('Price')
+plt.legend()
+plt.show()