An end-to-end Supervised Learning project utilizing Kaggle datasets for urban valuation.
This project is a comprehensive application of Supervised Machine Learning. Using high-quality historical real estate datasets from Kaggle, I developed a regression model that accurately predicts house prices in Bengaluru city of India. The workflow covers the entire lifecycle—from data exploration in Jupyter Notebooks to building a production-ready web interface.
Trained with Supervised Learning algorithms for high-accuracy price estimation.
Recognizes over 200 distinct neighborhood data points across the Bengaluru region.
Dynamic input handling for BHK, total square foot, and Bedrooms-bathroom count.
A sleek, intuitive dashboard built with HTML, CSS, and Bootstrap for seamless navigation.
I cleaned complex real-world datasets, removing outliers and noise to ensure the prediction model provides realistic market values.
I successfully converted static analysis from Jupyter Notebooks into a live, interactive web service that anyone can access.
The UI simplifies complex regression math into a three-step process, making data science accessible to non-technical users.
The project showcases a complete pipeline, proving the ability to handle data engineering, model training, and frontend design.