This project predicts house prices in Bangalore using a machine learning model. The project utilizes a linear regression model to estimate prices based on various input features like the number of bedrooms, bathrooms, total square footage, and location.
- Data Preprocessing: Handles missing data and applies one-hot encoding for categorical variables.
- Linear Regression Model: Trained to predict house prices based on input features.
- Prediction Function: Predicts house prices based on user inputs such as location, square footage, and the number of rooms.
- Exported Model: The trained model is saved as a pickle file for future use.
- Location Data: Location and feature information are exported in JSON format to aid prediction applications.
Clone the repository with git clone https://github.com/yourusername/house-price-prediction.git. Install dependencies with pip install -r requirements.txt.
To make a prediction, load the saved model and JSON column data, then call the predict_price() function, providing the necessary parameters like predict_price('Indira Nagar', 1000, 2, 2).
main.ipynb - Jupyter notebook containing all the code and steps to train the model. bangalore_home_prices_model.pickle - Saved machine learning model. columns.json - JSON file with location and feature information.
This code is provided as-is for educational and research purposes. You are free to use, modify, and distribute this code under the terms of your own licensing agreement.