House Price Prediction in Bengaluru

Project Overview

This project aims to predict house prices in Bengaluru using a Linear Regression model. The dataset contains various features affecting house prices, such as location, size, number of bedrooms, bathrooms, and total square footage. The goal is to build a predictive model that can estimate house prices based on these attributes.

Table of Contents

1. Project Overview
2. Folder Structure
3. Dataset
4. Installation
5. Model Development
6. Usage
7. Results
8. Conclusion
9. Future Work
10. Contact

Folder Structure

House-Price-Prediction-Model/  
├── code/              # Core scripts for data preprocessing and model training  
│   ├── preprocess.py  # Data cleaning and preprocessing scripts  
│   ├── train.py       # Model training and evaluation script  
│   └── utils.py       # Helper functions  
├── Dataset/           # Dataset used for the project  
│   └── Bengaluru_House_Data.csv  
├── client/            # React application for the frontend  
│   ├── public/  
│   ├── src/  
│   │   ├── App.js     # Main React component  
│   │   ├── components/  
│   │   │   └── Form.js  # Component for entering house details  
│   │   └── api.js     # API integration for server communication  
│   ├── package.json   # Dependencies for the React app  
│   └── README.md      # Frontend-specific documentation  
├── server/            # Flask application for the backend  
│   ├── server.py      # Main Flask app to serve predictions  
│   ├── requirements.txt  # Backend dependencies  
│   └── model.pkl      # Trained model file  
└── README.md          # Documentation for the project  

Dataset

The dataset is sourced from Kaggle and contains detailed information about housing in Bengaluru. Key features include:

• Location: The area where the property is situated.
• Size: Number of bedrooms (BHK).
• Total_sqft: Total area of the house in square feet.
• Bath: Number of bathrooms.
• Price: The target variable representing the house price.

Installation

To set up the project on your local machine, follow these steps:

1. Clone the Repository

git clone https://github.com/Srinivaskoruprolu007/House-Price-Prediction-Model.git  
cd House-Price-Prediction-Model  

2. Install Dependencies

Backend Dependencies:

Navigate to the server/ folder and install the necessary libraries:

cd server  
pip install -r requirements.txt  

Frontend Dependencies:

Navigate to the client/ folder and install the required packages:

cd client  
npm install  

Model Development

Data Preprocessing

• Missing Values: Rows with critical missing values were either removed or imputed.
• Outlier Removal: Outliers that skewed results were identified and removed.
• Encoding Categorical Features: Categorical variables like Location were transformed using one-hot encoding.
• Feature Scaling: Scaling was applied to continuous variables for better model performance.

Model Training

• The model was developed using Linear Regression from the scikit-learn library.
• GridSearchCV was used to optimize hyperparameters and ensure model generalizability.

Usage

Backend:

The backend is implemented using Flask and serves predictions via a REST API.

1. Start the Flask Server:
   Navigate to the server/ folder and run:

   python server.py  

   The server will be accessible at http://127.0.0.1:5000/.

2. Test the API:
   Use tools like Postman or cURL to send house details (e.g., location, size, total square footage) to the API endpoint /predict and get the predicted house price.

Frontend:

The frontend is a React application for user interaction.

1. Start the React Application:
   Navigate to the client/ folder and run:

   npm start  

   The React app will open in your browser at http://localhost:3000/.

2. Predict House Prices:
   Use the form provided in the app to input house details and view the predicted price. The app sends data to the Flask server and displays the results in real time.

Results

The Linear Regression model achieved the following metrics on the test data:

• R² Score: 0.75
• Mean Absolute Error (MAE): 10.32 Lakhs

The model demonstrates a good predictive capability, although further enhancements can be made.

Conclusion

This project illustrates the application of a Linear Regression model for predicting house prices in Bengaluru. It highlights key data preprocessing steps and model training techniques.

Future Work

Future enhancements could include:

• Feature Engineering: Incorporating additional features such as proximity to amenities or age of the property.
• Advanced Models: Exploring models like Random Forest or XGBoost for improved accuracy.
• Web Application: Enhancing the React and Flask integration for a more seamless user experience.

Contact

For any questions or feedback, please reach out at [email protected].