This repository contains a simple House Price Prediction model implemented using Python. The project follows a structured process, including data cleaning, model development using Ridge regression, and the creation of a web-based user interface using Flask.
Key Components
Data Cleaning: The model utilizes a dataset from Kaggle (Seattle House Price Prediction). The dataset undergoes cleaning to handle missing values, categorical data, and other preprocessing steps.
Model Development: The machine learning model is implemented using Ridge regression, leveraging the scikit-learn library. The trained model is saved for later use.
Flask Web Application: The project incorporates a Flask web application, providing a user-friendly interface for predicting house prices. Users can input details such as the number of bedrooms, bathrooms, house size, and zip code to receive a price prediction.
Usage Clone the repository:
git clone https://github.com/yourusername/HousePrice_Prediction.git cd HousePrice_Prediction
Install dependencies:
pip install -r requirements.txt
Run the Flask application:
python main.py
Open your web browser and visit http://127.0.0.1:5000/ to interact with the House Price Prediction interface.
Datasets Used Seattle House Price Prediction Dataset [Kaggle] Feel free to explore and adapt the project for your own use. If you have any questions or suggestions, please create an issue or reach out to yourusername. Happy coding!