House-Price-Prediction

A machine learning project for predicting house prices in Bengaluru using a trained Random Forest model.

Overview

This repository contains the data, preprocessing, model artifacts, and Streamlit app for a house price prediction application.

Files

• app.py - Main Streamlit app for user input and price prediction.
• Bengaluru_House_Data.csv - Original dataset used for analysis and model training.
• cleaned_df.csv - Cleaned dataset prepared for training.
• model_columns.joblib - Saved list of feature columns used by the model.
• rf_model.joblib - Saved Random Forest regression model.
• requirements.txt - Python dependencies needed to run the app.
• eda.ipynb - Exploratory data analysis notebook.
• design.streamlit/config.toml - Streamlit app configuration.

Installation

1. Clone the repository:

   git clone https://github.com/sumith25-dev/House-Price-Prediction.git
   cd "House Price Prediction"

2. Create and activate a virtual environment:

   python -m venv venv
   .\venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

Usage

Run the Streamlit app:

streamlit run app.py

Then open the provided local URL in your browser to use the house price prediction interface.

Notes

• The model is trained for Bengaluru house pricing and may not generalize to other cities.
• Keep the model_columns.joblib and rf_model.joblib files in the same directory as app.py.