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. Dataset
3. Installation
4. Model Development
5. Usage
6. Results
7. Conclusion
8. Future Work
9. Contact

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

Ensure you have Python 3.x installed. Install the required libraries using:

pip install -r requirements.txt

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

To run the model and make predictions:

1. Run the Python Script: Execute the following command:

   python house_price_prediction.py

2. Make Predictions: Modify the script to input new house details for price predictions.

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: Creating a web interface using Flask or Streamlit for interactive predictions.

Contact

For any questions or feedback, please reach out at srinivasg3112@gmail.com.