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House Price Prediction Project

This project is a House Price Prediction Application developed for a final year project. It uses machine learning to predict house prices based on user input. The application has a React.js frontend and a Flask backend, which uses a pre-trained machine learning model.

Features

  • Predict house prices based on input features such as bedrooms, bathrooms, and house condition.
  • Modern and responsive user interface built with React.js.
  • Scalable Flask API that serves predictions from a pre-trained model.
  • Easily deployable on localhost for testing and demonstrations.

Project Structure

Backend

  • model/: Contains the trained machine learning model (house_price_model.pkl) and the scaler (scaler.pkl).
  • notebook/: Jupyter notebooks used for training the machine learning model.
  • app.py: Flask application that serves as the API.
  • requirements.txt: List of dependencies needed for the backend.

Frontend

  • public/: Public assets for the React application.
  • src/: Source files for the React application.
    • App.js: Main component handling the UI.
    • components/: Reusable components.
  • package.json: Configuration file listing dependencies and scripts for the frontend.

How to Run the Project

Step 1: Clone the Repository

Make sure you have Git installed on your system. Open your terminal and run:

git clone https://github.com/Rishiraj8/house_prediction.git
cd House

Step 2: Set Up the Backend

  1. Navigate to the backend folder:

    cd backend

  2. Create and activate a Python virtual environment (optional but recommended):

    python -m venv venv
source venv/bin/activate  # On Windows, use venv\Scripts\activate

  3. Install the required Python dependencies:

    pip install -r requirements.txt

  4. Start the Flask server:

    python app.py
    • The Flask server will now run on http://127.0.0.1:5000 (localhost).
    • Keep this terminal running.

Step 3: Set Up the Frontend

  1. Open a new terminal and navigate to the frontend folder:

    cd frontend

  2. Install the required Node.js dependencies:

    npm install

  3. Start the React development server:

    npm run dev
    • The React app will now run on http://127.0.0.1:5173 (localhost).
    • Keep this terminal running.

Step 4: Use the Application

  1. Open your browser and go to http://127.0.0.1:5173.
  2. Enter the required details in the form (e.g., number of bedrooms, bathrooms, etc.).
  3. Click the Predict Price button.
  4. The predicted house price will be displayed on the screen.

Notes

  • Ensure both the Flask backend and React frontend are running simultaneously.
  • If you encounter issues, check the terminal logs for errors.
  • You can modify the machine learning model or retrain it using the Jupyter notebook in the notebook/ folder.

Model Description

  1. Dataset:

    • The model was trained using a dataset (house_data.csv) containing features related to house properties and their respective prices.

  2. Features Used:

    • Number of bedrooms
    • Number of bathrooms
    • Living area (in square feet)
    • Condition of the house (scaled from 1 to 5)
    • Number of schools nearby

  3. Target Variable:

    • Price: The dependent variable representing the house price.

  4. Data Preprocessing:

    • The feature variables were scaled using StandardScaler to normalize the data. This ensures that features with varying magnitudes do not disproportionately influence the model.

  5. Model Used:

    • Random Forest Regressor: A machine learning ensemble model that uses multiple decision trees to predict the target variable.
      • Key Parameters:
        • n_estimators=100: The model uses 100 decision trees for making predictions.
        • random_state=42: Ensures reproducibility of results.
      • Why Random Forest:
        • Handles non-linear relationships well.
        • Resistant to overfitting when tuned properly.
        • Capable of handling feature importance, making it suitable for regression tasks like house price prediction.

  6. Model Evaluation:

    • R² Score (Coefficient of Determination):
      • Training R² Score: Measures how well the model fits the training data.
      • Testing R² Score: Indicates the model's predictive performance on unseen data.

  7. Model Saving:

    • The trained model (house_price_model.pkl) and the scaler (scaler.pkl) were serialized and saved for future use. These files are located in the backend\model directory.

Acknowledgments

This project was developed as a final year project for a school friend of mine.