BANGALORE HOUSE PRICE PREDICTION

Abstract

1. Introduction

This project features Bangalore House Price Prediction, a machine learning study conducted as part of an industrial internship. It explores the practical application of regression analysis in real estate economics.

By leveraging Scikit-learn, the system models the real estate landscape where an algorithm learns the relationship between independent variables (Location, Sqft, BHK) and the dependent variable (Price). The model is served via a Flask web server for real-time estimation.

A machine learning study demonstrating the application of Multivariate Regression algorithms to estimate real estate prices with high precision based on structural parameters.

2. Objective

Machine Learning Project to Predict House Prices in Bangalore.

This project demonstrates how Python, Machine Learning, Flask can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

• Runtime: Python 3.x
• Machine Learning: Scikit-learn
• Data Manipulation: Pandas, NumPy
• Visualization: Matplotlib
• Web Framework: Flask

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/Amey-Thakur/BANGALORE-HOUSE-PRICE-PREDICTION.git
cd BANGALORE-HOUSE-PRICE-PREDICTION

# Clone the repository
git clone https://github.com/Amey-Thakur/BANGALORE-HOUSE-PRICE-PREDICTION.git
cd BANGALORE-HOUSE-PRICE-PREDICTION

# Navigate to Source Code directory
cd "Source Code"

# Install dependencies
pip install pandas numpy matplotlib scikit-learn flask

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by Amey-Thakur and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.