🏡 House Price Prediction

This project aims to predict house prices using a linear regression model trained on a selected subset of features from the Ames Housing dataset. It uses clean machine learning practices including preprocessing pipelines and evaluation via cross-validation.

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📌 Project Summary

• 🎯 Goal: Predict SalePrice of houses using features like GrLivArea, BedroomAbvGr, and FullBath.
• 🧠 Model: Linear Regression
• ⚙️ Tools Used: scikit-learn, pandas, numpy, PowerTransformer, StandardScaler, Pipeline, ColumnTransformer

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📁 Dataset

• train.csv: Training data with features and target SalePrice
• test.csv: Testing data used for predictions
• Features used:
  • GrLivArea (Above ground living area in square feet)
  • BedroomAbvGr (Number of bedrooms above ground)
  • FullBath (Number of full bathrooms)

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🔧 Workflow

✅ 1. Preprocessing

• Applied PowerTransformer (Yeo-Johnson) to normalize skewed data
• Scaled features using StandardScaler
• Combined steps using ColumnTransformer and Pipeline

✅ 2. Modeling

• Built a regression pipeline using LinearRegression
• Evaluated performance using:
  • Mean Squared Error (MSE)
  • R² Score

✅ 3. Cross-Validation

• Performed 10-fold cross-validation using cross_val_score
• Averaged scores for robust model evaluation

✅ 4. Output

• Generated predictions on test data
• Preserved original Id from input dataset
• Created a final DataFrame with:
  • Id
  • Predicted sale price
• Exported as test_output.csv

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🚀 How to Run

1. Clone the repo:

git clone https://github.com/yourusername/house-price-prediction.git
cd house-price-prediction

2. Install requirements:

pip install -r requirements.txt
Run the Jupyter notebook:

3. Run the jupyter notebook:

jupyter notebook

📦 Dataset Source

This project uses data from the Kaggle House Prices - Advanced Regression Techniques competition.

📥 View Dataset on Kaggle

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📬 Contact

Created with ❤️ by Swarnabha Ghosh
Feel free to reach out or contribute!
Email: swarnabha983@gmail.com