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README.md
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# Bangalore House Price Prediction
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## Overview
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This project aims to predict house prices in Bangalore using a machine learning model. The model takes into account various features such as location, total square footage, number of bathrooms, and number of bedrooms (BHK). The project includes a web application built using Streamlit to provide an interactive user interface for making predictions.
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>Some files are not available on GitHub due to the large file size. you can get it [HERE](https://huggingface.co/sujoy0011/Bengaluru_House_Price_Prediction/tree/main)
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## Table of Contents
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- [Overview](#overview)
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- [Project Structure](#project-structure)
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- [Installation](#installation)
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- [Usage](#usage)
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- [Dataset](#dataset)
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- [Model](#model)
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- [Results](#results)
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## Project Structure
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The project consists of the following files and directories:
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- `app.py`: The main script for the Streamlit web application.
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- `Bengaluru_House_prediction.ipynb`: Jupyter notebook containing the data analysis and model training process.
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- `random_forest_house_price_model.pkl`: Serialized machine learning model. `NOT AVAILABLE ON GITHUB DUE TO THE LARGE FILE SIZE.`
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- `dataset.pkl`: Serialized dataset used for training the model.
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## Installation
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1. Clone the repository:
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```bash
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git clone https://github.com/yourusername/BangaloreHousePricePrediction.git
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cd BangaloreHousePricePrediction
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```
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2. Create a virtual environment and activate it:
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```bash
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python -m venv venv
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source venv/bin/activate # On Windows, use `venv\Scripts\activate`
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```
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3. Install the required packages:
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```bash
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pip install -r requirements.txt
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```
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4. Ensure you have Streamlit installed:
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```bash
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pip install streamlit
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```
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## Usage
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To run the web application, execute the following command:
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```bash
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streamlit run app.py
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```
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This will start the Streamlit server, and you can access the web application in your browser at `http://localhost:8501`.
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## Dataset
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The dataset used for this project is a comprehensive collection of housing data from Bangalore. It includes features such as location, total square footage, number of bathrooms, and number of bedrooms (BHK). The dataset is serialized and stored in `dataset.pkl`.
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## Model
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The machine learning model used in this project is a Random Forest Regressor. The model is trained on the dataset to predict house prices based on the input features. The trained model is serialized and stored in `random_forest_house_price_model.pkl`.
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## Results
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The web application allows users to input the location, total square footage, number of bathrooms, and number of bedrooms to get a predicted house price in Bangalore. The predicted price is displayed in lakhs.
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