Exploratory Data Analysis (EDA) Project

This repository is a comprehensive collection of resources, code, and datasets for performing Exploratory Data Analysis (EDA) on a variety of real-world datasets. It is designed for data science learners and practitioners who want to explore, visualize, and gain insights from data using Python and Jupyter Notebooks.

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📁 Repository Structure

File/Folder	Description
Plotters.ipynb	Notebook for general plotting and visualization techniques.
Problem1.ipynb	EDA notebook for Problem 1 (see notebook for details).
Pronlem2.ipynb	EDA notebook for Problem 2 (typo: should be 'Problem2').
Pronlem3.ipynb	EDA notebook for Problem 3 (typo: should be 'Problem3').
Problem4.ipynb	EDA notebook for Problem 4.
Problem5.ipynb	EDA notebook for Problem 5.
Problem6.ipynb	EDA notebook for Problem 6.
Tesla.csv	Tesla stock data for analysis.
ipl_batting.csv	IPL cricket batting data.
netflix_titles.csv	Netflix titles dataset.
train.csv	Generic training dataset (context in notebooks).
weather.csv	Weather data for EDA.
world_population.csv	World population statistics.
all_stocks_5yr.csv	5 years of stock data for analysis.
Connections.csv	LinkedIn connections data.
DelayedFlights.csv	US flight delay dataset.
insurance.csv	Insurance data for EDA.
IRIS.csv	Classic Iris flower dataset.
Problem7.ipynb	EDA notebook for Problem 7.
Problem8.ipynb	EDA notebook for Problem 8.
Problem9.ipynb	EDA notebook for Problem 9.
Problem10.ipynb	EDA notebook for Problem 10.
Problem11.ipynb	EDA notebook for Problem 11.
Rich_Media.csv	LinkedIn post/media data.
LICENSE	License file for this repository.
README.md	This documentation file.

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📝 Notebooks

Each notebook is self-contained and focuses on a specific dataset or EDA technique. They include:

• Data loading and cleaning
• Exploratory visualizations (histograms, scatter plots, bar charts, etc.)
• Statistical summaries
• Insights and observations

Refer to the top of each notebook for a summary of its purpose and the dataset it uses.

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📊 Datasets

The repository includes several CSV files for hands-on EDA practice. These datasets cover topics such as stock prices, sports analytics, entertainment, weather, and demographics. You can use them directly in the provided notebooks or for your own analysis.

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🚀 Getting Started

1. Clone the repository:

   git clone https://github.com/Dhanas3kar/EDA.git
   cd EDA

2. Set up a Python environment:
   • It is recommended to use a virtual environment:

     python -m venv venv
     venv\Scripts\activate  # On Windows
     # or
     source venv/bin/activate  # On macOS/Linux

3. Install required packages:
   • Most notebooks use pandas, matplotlib, and seaborn. Install them with:

     pip install pandas matplotlib seaborn

4. Open notebooks:
   • Use JupyterLab, Jupyter Notebook, or VS Code to open and run the .ipynb files.

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💡 Usage

• Run the notebooks cell by cell to see the analysis and visualizations.
• Modify the code to try your own EDA ideas or apply techniques to new datasets.
• Use the provided datasets for practice or coursework.

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🤝 Contributing

Contributions are welcome! You can:

• Fix typos or improve documentation
• Add new datasets or notebooks
• Suggest new EDA techniques or visualizations

To contribute, fork the repository, make your changes, and submit a pull request.

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📄 License

This project is licensed under the terms of the MIT License. See the LICENSE file for details.

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📬 Contact

For questions, suggestions, or collaboration, please open an issue or contact the repository owner via GitHub.