🏠 House Price Prediction with Explainability

📌 Project Overview

This project predicts house prices using the Ames Housing dataset. Multiple ensemble regression models are trained and evaluated, with feature correlation analysis for explainability.

📊 Dataset

• Ames Housing Dataset
• 2,900+ records
• 80+ features

🧹 Data Preprocessing

• Dropped high-missing categorical columns
• Mean imputation for numerical features
• Mode imputation for categorical features
• One-hot encoding
• Feature scaling using StandardScaler

🧠 Feature Selection

• Pearson correlation with target variable
• Top 20 correlated features selected

🤖 Models Used

• Random Forest Regressor
• Gradient Boosting Regressor
• HistGradientBoosting Regressor

📈 Evaluation Metrics

• R² Score
• RMSE (Root Mean Squared Error)

🏆 Results

Model	Test R²	RMSE
Random Forest	XX	XX
Gradient Boosting	XX	XX
HistGradientBoosting	XX	XX

🛠️ Tech Stack

• Python
• Pandas, NumPy
• Scikit-learn
• Matplotlib, Seaborn

🚀 How to Run

pip install -r requirements.txt
python src/train_models.py