This project applies data science to real estate to help analyze and predict property trends. It includes data cleaning, feature engineering, and building machine learning models. A recommendation system suggests properties based on user preferences. Visualizations provide insights into market patterns. The final product is a complete pipeline with a simple, user-friendly app.
- 🏠 Price Predictor: Machine learning model to estimate property prices in Gurgaon.
- 📊 Analytics Dashboard: Visualizations for market trends, property distributions, and geospatial analysis.
- 💡 Apartment Recommender: Content-based system to suggest apartments based on user preferences.
- 🔄 Data Pipeline: Fully reproducible DVC pipeline for data processing, model training, and evaluation.
The project evaluates various regression models for predicting property prices, including Linear Regression, Support Vector Regression (SVR), Random Forest Regressor, Multi-layer Perceptron (MLP), LASSO Regression, Ridge Regression, Gradient Boosting Regressor, Decision Tree Regressor, K-Nearest Neighbors Regressor, and ElasticNet Regression.
- Clone the repository to your local machine.
- Install the required dependencies specified in the
requirements.txtfile. - Run the main application file to launch the user interface and explore features, predictions, and recommendations.
Special thanks to Nitish Singh - CampusX for guidance and support throughout the project.
