Feature-Selection

This project intends to show the ways we can perform feature selection on our data.

Skills that are covered in the project:

1. Feature Selection with Filtering Method
2. Constant, Quasi Constant and Duplicate Feature Removal
3. Correlated Feature Removal
4. Feature Selection Based on Univariate ROC_AUC for Classification and MSE for Regression
5. Feature Selection Based on Mutual Information (Entropy) Gain for Classification and Regression
6. Feature Selection Based on Univariate (ANOVA) Test for Classification
7. Feature Selection using Fisher Score and Chi2 (χ2) Test
8. Feature Dimention Reduction Using LDA and PCA with Python | Principal Component Analysis in Feature Selection
9. Step Forward, Step Backward and Exhaustive Feature Selection | Wrapper Method
10. Use of Linear and Logistic Regression Coefficients with Lasso (L1) and Ridge (L2) Regularization for Feature Selection
11. Recursive Feature Elimination (RFE) by Using Tree Based and Gradient Based Estimators