House Price Predictor

Welcome to the House Price Predictor repository! This project leverages machine learning and a Streamlit-powered web application to predict house prices based on various features. Whether you're a data enthusiast, student, or real estate professional, this repository shows how predictive modeling can be applied to real-world housing data with an interactive user interface.

Table of Contents

• Project Overview
• Features
• Installation
• Usage
• Data
• Model
• Evaluation
• Contributing
• License

Project Overview

The House Price Predictor trains machine learning models to estimate the price of a house given its attributes (location, square footage, number of rooms, etc.). The project demonstrates the full pipeline: data preprocessing, feature engineering, model training, evaluation, and prediction — all accessible through a Streamlit web app.

Features

• Data cleaning and preprocessing
• Exploratory data analysis (EDA) with visualizations
• Feature engineering and selection
• Multiple regression models (e.g., Linear Regression, Random Forest, XGBoost)
• Model evaluation and comparison
• Interactive Streamlit web app for predictions
• User-friendly input forms and visualization

Installation

1. Clone the repository:

   git clone https://github.com/mani-shika/house-price-predictor.git
   cd house-price-predictor

2. Install dependencies:

   pip install -r requirements.txt

Usage

1. Prepare the data:
   Place your dataset in the data/ directory. Make sure the format matches the expected input features.

2. Train the model:
   Run the training script (replace with actual script name if different):

   python train.py

3. Launch the Streamlit app:

   streamlit run app.py

   The app will open in your browser, allowing you to input house features and see predicted prices.

Data

• The project uses housing data with features such as:
  • Location
  • Size (square footage)
  • Number of bedrooms and bathrooms
  • Year built
  • Amenities
• Sample datasets and instructions are provided in the data/ folder.

Model

• Various regression algorithms implemented and compared
• Model hyperparameter tuning for optimal performance
• Model artifacts saved in the models/ directory

Evaluation

• Performance metrics include RMSE, MAE, and R² score
• Visualization of predictions vs actual prices
• Model comparison results displayed in reports

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a new branch (git checkout -b feature-name)
3. Commit your changes
4. Push to your branch (git push origin feature-name)
5. Open a pull request

License

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

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If you have any questions, feel free to open an issue or contact the repository owner.