https://github.com/anujdutt9/Feature-Selection-for-Machine-Learning

Methods with examples for Feature Selection during Pre-processing in Machine Learning.
https://github.com/anujdutt9/Feature-Selection-for-Machine-Learning

correlation feature-selection machine-learning python36

Last synced: about 1 month ago
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Methods with examples for Feature Selection during Pre-processing in Machine Learning.

• Host: GitHub
• URL: https://github.com/anujdutt9/Feature-Selection-for-Machine-Learning
• Owner: anujdutt9
• Created: 2018-06-02T20:47:54.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2020-05-24T22:41:23.000Z (over 4 years ago)
• Last Synced: 2024-11-13T03:34:46.392Z (about 1 month ago)
• Topics: correlation, feature-selection, machine-learning, python36
• Language: Jupyter Notebook
• Homepage:
• Size: 345 KB
• Stars: 364
• Watchers: 30
• Forks: 163
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-machine-learning-resources - **[Tutorial - Selection-for-Machine-Learning?style=social) (Table of Contents)

README

        
# 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](https://www.kaggle.com/c/santander-customer-satisfaction)

**2.** [BNP Paribas Cardif Claims Management Dataset](https://www.kaggle.com/c/bnp-paribas-cardif-claims-management)

**3.** [Titanic Disaster Dataset](https://www.kaggle.com/c/titanic/data)

**4.** [Housing Prices Dataset](https://www.kaggle.com/c/house-prices-advanced-regression-techniques)

# 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    |

| ----- | ----------------- | ------------------------------------------------------------------ | ------------ |