📝 Exploratory Data Analysis Projects using Python & SQL

This repository houses a diverse collection of 8 Exploratory Data Analysis (EDA) projects, each utilizing Python and SQL to delve deep into various datasets. From sales and profitability insights to geographical analyses, these projects demonstrate the power of data to uncover meaningful patterns and trends.

📚 Table of Contents

1. 🚗 Car Data Analysis

   • Description: Analysis of various factors affecting car prices, mileage, and overall market trends.
   • Tools: Python, Pandas, Matplotlib, SQL.
   • LInk: https://github.com/Malkanagouda/Exploratory-Data-Analysis-with-Python/tree/main/Car

2. 🎇 Diwali Sales Analysis

   • Description: Analyzing Diwali sales data to uncover consumer behavior and sales trends during the festive season.
   • Tools: Python, Pandas, Seaborn, SQL.
   • Link: https://github.com/Malkanagouda/Exploratory-Data-Analysis-with-Python/tree/main/Diwali_sales

3. 📈 Euromart Sales & Profitability Analysis

   • Description: Detailed examination of sales performance and profitability across different regions and product categories.
   • Tools: Python, Pandas, Matplotlib, SQL.
   • Link: https://github.com/Malkanagouda/Exploratory-Data-Analysis-with-Python/tree/main/Euromart

4. 🌍 Global Superstore Sales & Profitability Analysis

   • Description: Comprehensive analysis of global sales data, focusing on customer segmentation and regional performance.
   • Tools: Python, Pandas, Seaborn, SQL.
   • Link: https://github.com/Malkanagouda/Exploratory-Data-Analysis-with-Python/tree/main/Global_Store

5. 🎥 IMDb Movie Analysis

   • Description: Exploring IMDb data to identify trends in movie ratings, genres, and box office success.
   • Tools: Python, Pandas, Matplotlib, SQL.
   • Link: https://github.com/Malkanagouda/Exploratory-Data-Analysis-with-Python/tree/main/IMDB%20Movie

6. 🏏 Full Fledge IPL Data Analysis (2008-2024)

   • Description: In-depth analysis of IPL data spanning from 2008 to 2024, including player performance, team statistics, and match outcomes.
   • Tools: Python, Pandas, Seaborn, SQL.
   • Link: https://github.com/Malkanagouda/Exploratory-Data-Analysis-with-Python/tree/main/IPL/IPL%202008-2024

7. 🚢 Titanic Data Analysis

   • Description: Investigating the factors that influenced passenger survival rates on the Titanic.
   • Tools: Python, Pandas, Matplotlib, Seaborn, SQL.
   • Link: https://github.com/Malkanagouda/Exploratory-Data-Analysis-with-Python/tree/main/Titanic

8. 🍽️ Zomato Geographical Analysis

   • Description: Geographical analysis of Zomato's restaurant data, focusing on location-based trends and customer preferences.
   • Tools: Python, Pandas, Seaborn, SQL.
   • Link: https://github.com/Malkanagouda/Exploratory-Data-Analysis-with-Python/tree/main/Zomato

⚙️ Requirements

Requirement
Python 3.x
Pandas
Matplotlib
Seaborn
SQL (MySQL, SQLite)
Jupyter Notebook (optional)
VS Code

🛠️ Installation

1. Clone the repository:

   https://github.com/Malkanagouda/Exploratory-Data-Analysis-with-Python

🤝 Contributing Contributions are welcome! If you have any suggestions or improvements, feel free to submit a pull request.

📜 License This project is licensed under the MIT License - see the LICENSE file for details.