🏡 House Price Prediction – End-to-End ML Project

📌 Overview

This project implements an end-to-end Machine Learning pipeline for predicting house prices. It demonstrates the ability to go from raw data → insights → predictive models → deployable artifacts

🎯 Objectives

• Build a predictive model for house prices.
• Showcase data cleaning, feature engineering, and EDA.
• Compare baseline (Linear Regression) with advanced ML models (Random Forest, XGBoost, Gradient Boosting).
• Evaluate models using industry-standard metrics (R², MAE, RMSE).
• Deliver clear visualizations and business-ready insights.

---

🔑 Key Features

• EDA & Visualization: Correlation heatmaps, distribution plots, actual vs predicted plots, feature importance.
• Feature Engineering: Derived features like house_age, price_per_sqft.
• Modeling: Linear Regression, Random Forest, Gradient Boosting, XGBoost with hyperparameter tuning.
• Evaluation: R², MAE, RMSE with visualization of residuals.
• Reproducibility: Clean Colab/Jupyter Notebook with modular pipeline.
• Scalability: Extensible for larger datasets and deployable via Flask/FastAPI + cloud.

---

🛠️ Tech Stack

• Languages: Python (NumPy, Pandas, Matplotlib, Seaborn)
• ML Libraries: Scikit-learn, XGBoost
• Tools: Jupyter/Colab, GitHub
• Extensions (optional): Flask/FastAPI for API deployment, Docker for containerization, AWS/GCP for cloud scaling

---

📊 Results & Insights

• Top predictive features: Location, square footage, number of rooms.
• Tree-based models (Random Forest, XGBoost) outperformed linear regression.
• Strong correlation between square footage & house price.
• Visualizations provide clear business insights for real estate pricing strategies.

---