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Boaton Analysis Using Python

This project focuses on analyzing and modeling the Boston Housing Dataset using Python. It includes data preprocessing, visualization, feature selection, and applying a linear regression model to predict housing prices based on various features.

📊 Features

• Load and explore the Boston Housing Dataset
• Data cleaning and normalization
• Correlation analysis and visualization
• Linear Regression model implementation
• Model evaluation using metrics like R² and MSE

🛠️ Technologies Used

• Python 3.x
• NumPy
• Pandas
• Matplotlib & Seaborn
• Scikit-learn

📁 Project Structure

boaton_analysis-using-python/
├── boston_data_analysis.ipynb   # Main analysis notebook
├── boston.csv                   # Dataset (if provided)
└── README.md                    # Project documentation

🚀 Getting Started

1. Clone the repository:

git clone https://github.com/nurbek18/boaton_analysis-using-python.git
cd boaton_analysis-using-python

2. Install the required packages (preferably inside a virtual environment):

pip install -r requirements.txt

If requirements.txt is not available, install manually:

pip install numpy pandas matplotlib seaborn scikit-learn

3. Launch the Jupyter Notebook:

jupyter notebook boston_data_analysis.ipynb

📈 Sample Visualizations

This project includes:

• Heatmap of feature correlations
• Scatter plots between features and target variable
• Predicted vs Actual values plot for the regression model

🤝 Contributions

Feel free to open issues or submit pull requests to contribute or suggest improvements!

📄 License

This project is open-sourced under the MIT License.

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