anujdutt9 / Feature Selection For Machine Learning
Methods with examples for Feature Selection during Pre-processing in Machine Learning.
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Feature Selection for Machine Learning
This repository contains the code for three main methods in Machine Learning for Feature Selection i.e. Filter Methods, Wrapper Methods and Embedded Methods. All code is written in Python 3.
Status: Ongoing
Requirements
1. Python 3.5 +
2. Jupyter Notebook
3. Scikit-Learn
4. Numpy [+mkl for Windows]
5. Pandas
6. Matplotlib
7. Seaborn
8. mlxtend
Datasets
1. Santander Customer Satisfaction Dataset
2. BNP Paribas Cardif Claims Management Dataset
Filter Methods
| S.No. | Name | About | Status |
|---|---|---|---|
| 1. | Constant Feature Elimination | This notebook explains how to remove the constant features during pre-processing step. | Completed |
| 2. | Quasi-Constant Feature Elimination | This notebook explains how to get the Quasi-Constant features and remove them during pre-processing. | Completed |
| 3. | Duplicate Features Elimination | This notebook explains how to find the duplicate features in a dataset and remove them. | Completed |
| 4. | Correlation | This notebook explains how to get the correlation between features and between features and target and choose the best features. | Completed |
| 5. | Machine Learning Pipeline | This notebook explains how to use all the above methods in a ML pipeline with performance comparison. | Completed |
| 6. | Mutual Information | This notebook explains the concept of Mutual Information using classification and Regression to find the best features from a dataset. | Completed |
| 7. | Fisher Score Chi Square | This notebook explains the concept of Fisher Score chi2 for feature selection. | Completed |
| 8. | Univariate Feature Selection | This notebook explains the concept of Univariate Feature Selection using Classification and Regression. | Completed |
| 9. | Univariate ROC/AUC/MSE | This notebook explains the concept of Univariate Feature Selection using ROC AUC scoring. | Completed |
| 10. | Combining all Methods | This notebook compares the combined performance of all methods explained. | Completed |
Wrapper Methods
| S.No. | Name | About | Status |
|---|---|---|---|
| 1. | Step Forward Feature Selection | This notebook explains the concept of Step Forward Feature Selection. | Completed |
| 2. | Step Backward Feature Selection | This notebook explains the concept of Step Backward Feature Selection. | Completed |
| 3. | Exhaustive Search Feature Selection | This notebook explains the concept of Exhaustive Search Feature Selection. | Completed |
Embedded Methods
| S.No. | Name | About | Status |
|---|
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