https://github.com/prarthana-singh/bangalore-house-price-predictor
🏡 Bangalore House Price Prediction – A Machine Learning model to predict house prices in Bangalore using real estate data. Built with Linear Regression, Python, Pandas, NumPy, and Scikit-Learn.
https://github.com/prarthana-singh/bangalore-house-price-predictor
data-analysis eda house-price-prediction linear-regression machine-learning numpy pandas python real-estate regression scikit-learn
Last synced: 8 months ago
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🏡 Bangalore House Price Prediction – A Machine Learning model to predict house prices in Bangalore using real estate data. Built with Linear Regression, Python, Pandas, NumPy, and Scikit-Learn.
- Host: GitHub
- URL: https://github.com/prarthana-singh/bangalore-house-price-predictor
- Owner: Prarthana-Singh
- Created: 2025-02-02T11:42:37.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-03-02T20:38:13.000Z (8 months ago)
- Last Synced: 2025-03-02T21:28:51.323Z (8 months ago)
- Topics: data-analysis, eda, house-price-prediction, linear-regression, machine-learning, numpy, pandas, python, real-estate, regression, scikit-learn
- Language: Python
- Homepage: https://bangalore-house-price-predictor-8zg0.onrender.com
- Size: 5.33 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🏠Bangalore House Price Prediction
## Live Demo
[Try Here](https://bangalore-house-price-predictor-8zg0.onrender.com)
## Project Overview
This project predicts house prices in Bangalore based on features such as location, square footage, number of bedrooms, and bathrooms. The model is built using **Linear Regression** and deployed with **Streamlit** for user-friendly interaction.
## Dataset
The dataset includes:
- **Location**
- **Total square feet**
- **BHK (Number of bedrooms)**
- **Bathrooms**
- **Price (Target variable)**
## Installation
Ensure you have the necessary dependencies installed:
```bash
pip install pandas numpy scikit-learn streamlit pickle-mixin
```
## Implementation Steps
1. **Data Preprocessing**: Handle missing values, remove outliers, and perform feature engineering.
2. **Model Training**: Train a Linear Regression model.
3. **Model Evaluation**: Evaluate the model using R² score.
4. **Deployment**: Deploy using Streamlit.
## How to Use the Repository
1. **Clone the repository**:
```bash
git clone
cd
```
2. **Install dependencies**:
```bash
pip install -r requirements.txt
```
3. **Run the application**:
```bash
streamlit run app.py
```
4. **Interact with the application**:
- Enter the total square feet, number of BHK, and bathrooms.
- Click on **Predict Price** to get the estimated house price.
## Running the Application
To start the application, run the following command:
```bash
streamlit run app.py
```
Then, open the provided local URL in your browser.
## Demo Screenshots
## Conclusion
This project successfully predicts Bangalore house prices using **Linear Regression** and deploys it with **Streamlit** for easy interaction. 🚀