House Price Prediction Model

I need a Python-based machine-learning model that accurately predicts residential property prices from variables such as floor area, bedroom count, location, and any other relevant features you recommend. The results will feed directly into our existing business dashboards for market analysis, so consistency and clarity of the outputs are essential. You will receive clean, well-labeled CSV files as the sole data source. I’m open to your suggestions on feature engineering, algorithm choice (e.g., gradient boosting, random forest, or neural networks), and validation strategy, provided the final model demonstrates strong generalisation on unseen data. Deliverables should include: • Fully commented Python code (Jupyter notebook or .py scripts) • Trained model file ready for deployment • A concise README explaining preprocessing steps, feature selection, and how to retrain or update the model in the future • A short report summarising performance metrics and insights discovered during analysis Please build with widely supported libraries such as pandas, scikit-learn, XGBoost, or similar so our in-house team can maintain and extend the solution after hand-off.