🏠 Bengaluru House Price Prediction

Project Overview

This project is a Machine Learning-based regression model that predicts residential property prices in Bengaluru using features such as location, total area (square feet), number of bedrooms (BHK), and bathrooms. The project demonstrates the complete machine learning workflow, including data preprocessing, feature engineering, model training, evaluation, and hyperparameter tuning.

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Dataset Used

Name: Bengaluru House Price Dataset

Source: Kaggle - Bengaluru House Price Data

Dataset Includes

• Property Location
• Total Square Feet
• Number of Bedrooms (BHK)
• Number of Bathrooms
• Price
• Other Property Attributes

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What I Did

• Cleaned and preprocessed 13,000+ housing records by handling missing values, removing outliers, and transforming categorical features.
• Performed feature engineering and one-hot encoding to improve model performance.
• Trained and evaluated multiple regression models including Linear Regression, Lasso Regression, and Decision Tree Regression.
• Applied GridSearchCV and cross-validation for model selection and hyperparameter tuning.
• Selected the best-performing Linear Regression model based on evaluation metrics.
• Built a predictive system capable of estimating Bengaluru house prices using key property attributes.

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Key Performance Indicators (KPIs)

KPI	Value
Dataset Size	13,000+ Records
Best Model	Linear Regression
Validation R² Score	0.845 (84.5%)
5-Fold Cross Validation Score	~0.82
Regression Models Evaluated	3

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Model Features

• Data Cleaning & Preprocessing
• Feature Engineering
• Outlier Detection & Removal
• One-Hot Encoding
• Regression Model Comparison
• Hyperparameter Tuning using GridSearchCV
• Cross-Validation
• House Price Prediction

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Tools & Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Jupyter Notebook
• GridSearchCV
• Pickle

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Business Value

• Enables data-driven estimation of residential property prices.
• Demonstrates the application of machine learning techniques to real-world real estate data.
• Provides insights into the key factors influencing house prices in Bengaluru.

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Conclusion

This project showcases the complete lifecycle of a machine learning model, including data preprocessing, feature engineering, model training, evaluation, and optimization. It demonstrates practical experience in building predictive analytics solutions using Python and Scikit-learn.

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