Bangalore House Price Predictor
A machine learning-powered web application that predicts house prices in Bangalore based on location, square footage, number of bedrooms (BHK), and bathrooms. The app is built using Flask and integrates a trained Ridge Regression model to give real-time price estimates.
Frontend: HTML5, CSS3, Jinja2 (Flask templating)
Backend: Python, Flask
Modeling: Scikit-learn (Ridge Regression), Pandas, Numpy
Data Handling: CSV, Pickle (.pkl)
Development Tools: Jupyter Notebook, VS Code
Dataset: Bangalore House Price Dataset
Format: CSV file
Fields used: location, total_sqft, bath, bhk
Preprocessing: OneHotEncoding for location, scaling and cleaning
📍 Dynamic Location Dropdown: Automatically loads all unique locations from dataset
🧠 ML Model Integration: Uses a trained Ridge Regression model for accurate predictions
💡 Real-Time Price Prediction: Instantly predicts price based on input features
🔁 Form Reset Button: Easily clear inputs with a single click
📈 Clean UI: Simple, user-friendly layout
⚙️ Scalable: Easily extendable for new cities or more features
💾 Offline & Lightweight: Works locally without heavy setup