This is a learning and experimentation project for doing data science analysis for the Kaggle - House Prices: Advanced Regression Techniques competition.
https://www.kaggle.com/c/house-prices-advanced-regression-techniques
./requirements.sh # Install/Update VirtualEnv
source venv/bin/activate # Source VirtualEnv
jupyter lab # Explore Jupyter Notebooks
./main.py # Execute Data Pipeline
kaggle competitions submit -c house-prices-advanced-regression-techniques -f data/submissions/LeastSquaresCorr.csv -m "sklearn.linear_model.LinearRegression() on fields: .corr() > 0.5"
| Analysis | Notebook |
|---|---|
| Initial Exploration of the Dataset | 1_dataset.ipynb |
| Correlation Charts | 2_correlations.ipynb |
| Baseline Linear Regression | 3_baseline_linear_regression.ipynb |
| Feature Encoding | 4_feature_encoding.ipynb |
| Sklearn linear_model Exploration | 5_sklearn_linear_model.ipynb |
| Quartic Feature Encoding | 6_quartic_feature_encoding.ipynb |
| Score | Rank | Class |
|---|---|---|
| 0.13014 | 1817 / 4926 | fastai.tabular_learner |
| 0.15502 | 3074 / 4375 | ARDFeatures() |
| 0.17628 | 3470 / 4375 | RidgeFeatures() |
| 0.17628 | 3493 / 4432 | LarsCVPolynomial() |
| 0.18379 | RidgeCVNormalizePolynomial() | |
| 0.20892 | 3751 / 4339 | LinearRegressionModel() |
| 0.21785 | LarsCVLinear() | |
| 0.80406 | FeatureEncoding() | |
| 2.29145 | LassoLarsSquared() | |
| 2.29145 | SquaredFeatureEncoding() | |
| 2.42903 | ElasticNetSquared() | |
| 2.87097 | PolynomialFeatureEncoding() |