House Price Prediction

This project implements a house price prediction system using Linear Regression. It is built as an end-to-end machine-learning project using Flask.

Overview

The goal of this project is to predict the price of houses based on various features such as location, total square feet area, number of bathrooms, and number of bedrooms.

Features

• Predicts house prices based on input features.
• Provides a web interface to interact with the prediction model.
• Uses a Linear Regression algorithm for prediction.
• Implements an end-to-end machine learning project.

Project Structure

The project is structured as follows:

• app.py: Flask web application for serving predictions.
• model/: Directory containing trained model and preprocessing objects.
• dataset/: Directory containing the dataset used for training.
• templates/: HTML templates for the web interface.

Installation

Setup

1. Clone the repository:

   git clone https://github.com/kindo-tk/house_price_prediction.git

2. Navigate to the project directory:

   cd .\house_price_prediction\

3. Create a virtual environment:

   python -m venv .venv

4. Activate the virtual environment:

   .venv\Scripts\activate

5. Install the required packages:

   pip install -r requirements.txt

6. Run the Flask application:

   python app.py

Usage

1. Access the web application by navigating to http://localhost:5000 in your web browser.
2. Enter the required details such as location, total square feet area, number of bathrooms, and number of bedrooms.
3. Click on the "Predict Price" button to get the predicted house price.

Technologies Used

• Python
• Flask
• HTML/CSS
• Bootstrap
• scikit-learn

License

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

Contact

For any inquiries or feedback, please contact:

• Tufan Kundu (LinkedIn)
• Email: [email protected]

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