Predicting House Prices: An End-to-End Regression Modelling Workflow Problem Statement Background Real estate pricing is influenced by numerous factors — from a property’s living area and number of rooms to its condition, location, and year of construction. In India’s rapidly evolving housing market, accurately predicting property prices can help real-estate platforms, financial institutions, and policy makers make data-driven decisions. As a data scientist at a real-estate analytics startup, your role is to develop a reliable, interpretable, and production-ready regression model that predicts house prices using historical housing data. The dataset (House Price India.csv) contains property-level attributes and sale prices across different Indian regions. Objective Your goal is to build a complete predictive modelling pipeline — starting from exploratory baselines to advanced regularized models — while adhering to professional data-science practices such as fair evaluation, data hygiene, and reproducible workflows. You will: Establish fair baselines using simple statistical predictors (mean, median, and group-wise averages). Build and interpret regression models: Start with Simple Linear Regression (SLR) using one strong predictor (e.g., living_area). Extend to Multiple Linear Regression (MLR) with key features like bedrooms, bathrooms, lot area, etc. Diagnose model performance through metrics such as RMSE, MAE, R², Adjusted R², MAPE, and residual analysis. Identify and handle multicollinearity using Variance Inflation Factor (VIF). Enhance model generalization with Regularization Techniques — Ridge, Lasso, and ElasticNet. Apply fair evaluation strategies using Cross-Validation (K-Fold, LOOCV, and TimeSeriesSplit) and demonstrate awareness of data leakage. Select and justify the “Champion Model”, validating its performance on unseen test data. Export your model artifacts (model file, predictions, and metrics) and optionally integrate with tools like PyCaret, MLflow, or W&B to simulate an industry-grade workflow. Deliverables A Jupyter Notebook or Python script containing: Clean, well-documented code for each modelling step. Summary tables and visualizations of metrics and residuals. A comparative leaderboard of models (SLR, MLR, Ridge, Lasso, ElasticNet). A short written report covering: Key insights from residual analysis and diagnostics. Explanation of how regularization improved model stability. Rationale for the final champion model selection. Saved artifacts: champion_model.joblib houseprice_predictions_test.csv test_metrics.json Expected Learning Outcomes By the end of this case study, you will be able to: Design and implement a complete regression modelling workflow from scratch. Understand the importance of baselines in performance benchmarking. Interpret regression coefficients and residual diagnostics. Apply regularization to improve model robustness and interpretability. Use cross-validation and leakage prevention to ensure fair model comparison. Export, evaluate, and document a production-ready machine learning model