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Boston Housing Price Prediction Project

Project Overview

This project aims to predict housing prices in the Boston area using various machine learning models. It leverages the Boston Housing dataset, applying preprocessing techniques, feature engineering, and model evaluation to understand and predict housing prices effectively.

Getting Started

Prerequisites

Before running this project, ensure you have the following installed:

• Python 3.8 or later
• pip (Python package manager)

Installation

1. Clone the repository to your local machine:

   git clone https://github.com/pramodyasahan/Boston-Housing-Project.git

2. Navigate to the project directory:

   cd Boston-Housing-Project

3. Install the required Python packages:

   pip install -r requirements.txt

Usage

To run the project and train the machine learning model, execute the main.py script with the necessary command-line arguments. For example:

python main.py --model_type linear_regression

Command-Line Arguments

• --data_path: Path to the raw dataset. Default is data/raw/HousingData.csv.
• --data_processed_path: Path to save the processed dataset. Default is data/processed/Clean_HousingData.csv.
• --model_path: Path to save the trained model. Default is models/linear_regression.joblib.
• --model_type: Type of model to train. Supported values include linear_regression, logistic_regression, svm, random_forest_regression, and decision_tree_regression.

Project Structure

Boston-Housing-Project/
├── data/
│   ├── raw/              # Original dataset
│   └── processed/        # Preprocessed dataset
├── src/
│   ├── data/             # Data loading and preprocessing scripts
│   ├── features/         # Feature engineering scripts
│   ├── models/           # Model training, prediction, and evaluation scripts
├── requirements.txt      # Python package dependencies
└── main.py               # Main script to run the project

Models

This project includes several machine learning models for predicting housing prices:

• Linear Regression
• Logistic Regression (for classification tasks related to housing)
• Support Vector Machine (SVM)
• Random Forest
• Decision Tree

Contributing

Contributions to improve the project are welcome. Please follow these steps to contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/AmazingFeature).
3. Commit your changes (git commit -m 'Add some AmazingFeature').
4. Push to the branch (git push origin feature/AmazingFeature).
5. Open a pull request.

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Project Link: https://github.com/pramodyasahan/Boston-Housing-Project

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