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Boston House Price Prediction Model

Overview

This repository contains code and resources for predicting house prices in Boston using machine learning techniques.

Dataset

The dataset used for this project is the Boston Housing dataset, which is included in the scikit-learn library. It contains information collected by the U.S Census Service concerning housing in the area of Boston, Massachusetts.

Code Structure

The repository is organized as follows:

  • data/: Contains the dataset used for training and testing.
  • models/: Contains the trained machine learning models.
  • src/: Contains the source code for data preprocessing, model training, and evaluation.
  • notebooks/: Contains Jupyter notebooks for data exploration, model development, and evaluation.

Dependencies

  • Python 3.x
  • scikit-learn
  • numpy
  • pandas
  • matplotlib
  • seaborn
  • Jupyter Notebook (for running the notebooks)

Usage

To train the model and evaluate its performance, follow these steps:

  1. Clone this repository to your local machine.
  2. Install the required dependencies using pip install -r requirements.txt.
  3. Navigate to the src/ directory.
  4. Run the train.py script to train the model.
  5. Use the trained model for prediction by running the predict.py script.
  6. Evaluate the model's performance using the evaluate.py script.

Results

The performance metrics of the trained model, such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared score, are available in the evaluation reports generated by the evaluate.py script.

Contributing

Contributions to improve the model or add new features are welcome. If you'd like to contribute, please fork the repository and create a pull request with your changes.

License

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