This book presents a machine learning-based approach to predict real estate prices in Pune using historical housing data. The system analyzes key factors such as location, total square footage, number of bedrooms (BHK), and number of bathrooms to estimate property prices accurately.

The project focuses on building a reliable predictive model using regression techniques, along with effective data preprocessing and feature engineering methods. The trained model is integrated into a user-friendly web application developed using Streamlit, allowing users to input property details and receive real-time price predictions.

In addition to prediction, the system incorporates a Power BI dashboard for visualizing market trends, feature importance, and price distribution, helping users better understand real estate dynamics.

This work demonstrates the practical application of machine learning in solving real-world problems and provides a useful tool for buyers, sellers, and real estate investors. It also serves as a valuable academic reference for students and researchers interested in data science and predictive analytics.