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Sure! Here's the English version of the README.md for your project boaton_analysis-using-python:

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
  1. 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
  1. 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.