🏡 House Price Prediction

A regression project to predict house prices using various machine learning models like Linear Regression, Random Forest, and XGBoost based on the Kaggle House Prices dataset.

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📂 Project Structure

house-price-prediction/
│
├── data/           # contains train.csv, test.csv
├── notebooks/      # Jupyter notebook for training and EDA
├── models/         # saved model (.pkl), train_columns
├── images/         # visualizations
├── main.py         # script to load model and predict
├── requirements.txt
└── README.md

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🚀 Models Used

Model	MAE	RMSE	R²
Linear Regression	24,351	34,012	0.81
Random Forest	18,921	28,345	0.89

✅ Best performing model: Random Forest

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🛠️ How to Run

1. Install dependencies

pip install -r requirements.txt

2. Run prediction script

python main.py

Make sure you have best_model.pkl and train_columns.pkl inside the models/ folder.

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📦 Requirements

• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn

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🧠 Future Work

• Feature engineering
• Model tuning (GridSearchCV)
• Streamlit web app for user input

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