{"id":19978181,"url":"https://github.com/anujdutt9/feature-selection-for-machine-learning","last_synced_at":"2025-04-06T11:10:08.148Z","repository":{"id":37743076,"uuid":"135851193","full_name":"anujdutt9/Feature-Selection-for-Machine-Learning","owner":"anujdutt9","description":"Methods with examples for Feature Selection during Pre-processing in Machine Learning.","archived":false,"fork":false,"pushed_at":"2020-05-24T22:41:23.000Z","size":353,"stargazers_count":364,"open_issues_count":0,"forks_count":164,"subscribers_count":29,"default_branch":"master","last_synced_at":"2025-03-30T10:07:20.969Z","etag":null,"topics":["correlation","feature-selection","machine-learning","python36"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/anujdutt9.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-06-02T20:47:54.000Z","updated_at":"2025-03-14T02:01:08.000Z","dependencies_parsed_at":"2022-07-12T16:44:26.793Z","dependency_job_id":null,"html_url":"https://github.com/anujdutt9/Feature-Selection-for-Machine-Learning","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/anujdutt9%2FFeature-Selection-for-Machine-Learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/anujdutt9%2FFeature-Selection-for-Machine-Learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/anujdutt9%2FFeature-Selection-for-Machine-Learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/anujdutt9%2FFeature-Selection-for-Machine-Learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/anujdutt9","download_url":"https://codeload.github.com/anujdutt9/Feature-Selection-for-Machine-Learning/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247471521,"owners_count":20944158,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["correlation","feature-selection","machine-learning","python36"],"created_at":"2024-11-13T03:31:27.808Z","updated_at":"2025-04-06T11:10:08.118Z","avatar_url":"https://github.com/anujdutt9.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Feature Selection for Machine Learning\n\n***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.***\n\n**Status:** Ongoing\n\n# Requirements\n\n**1. Python 3.5 +**\n\n**2. Jupyter Notebook**\n\n**3. Scikit-Learn**\n\n**4. Numpy [+mkl for Windows]**\n\n**5. Pandas**\n\n**6. Matplotlib**\n\n**7. Seaborn**\n\n**8. mlxtend**\n\n# Datasets\n\n**1.** [Santander Customer Satisfaction Dataset](https://www.kaggle.com/c/santander-customer-satisfaction)\n\n**2.** [BNP Paribas Cardif Claims Management Dataset](https://www.kaggle.com/c/bnp-paribas-cardif-claims-management)\n\n**3.** [Titanic Disaster Dataset](https://www.kaggle.com/c/titanic/data)\n\n**4.** [Housing Prices Dataset](https://www.kaggle.com/c/house-prices-advanced-regression-techniques)\n\n# Filter Methods\n\n| S.No. |       Name        |                           About                                    |    Status    |\n| ----- | ----------------- | ------------------------------------------------------------------ | ------------ |\n|  1.   | Constant Feature Elimination | This notebook explains how to remove the constant features during pre-processing step. | Completed |\n|  2.   | Quasi-Constant Feature Elimination | This notebook explains how to get the Quasi-Constant features and remove them during pre-processing. | Completed |\n|  3.   | Duplicate Features Elimination | This notebook explains how to find the duplicate features in a dataset and remove them. | Completed |\n|  4.   | Correlation       | This notebook explains how to get the correlation between features and between features and target and choose the best features. | Completed |\n|  5.   | Machine Learning Pipeline | This notebook explains how to use all the above methods in a ML pipeline with performance comparison. | Completed |\n|  6.   | Mutual Information | This notebook explains the concept of Mutual Information using classification and Regression to find the best features from a dataset. | Completed  |\n|  7.   | Fisher Score Chi Square | This notebook explains the concept of Fisher Score chi2 for feature selection.  | Completed |\n|  8.   | Univariate Feature Selection | This notebook explains the concept of Univariate Feature Selection using Classification and Regression. | Completed |\n|  9.   | Univariate ROC/AUC/MSE | This notebook explains the concept of Univariate Feature Selection using ROC AUC scoring.| Completed |\n| 10.   | Combining all Methods | This notebook compares the combined performance of all methods explained. | Completed |\n\n\n# Wrapper Methods\n\n| S.No. |       Name        |                           About                                    |    Status    |\n| ----- | ----------------- | ------------------------------------------------------------------ | ------------ |\n| 1.    | Step Forward Feature Selection | This notebook explains the concept of Step Forward Feature Selection. | Completed |\n| 2.    | Step Backward Feature Selection | This notebook explains the concept of Step Backward Feature Selection. |Completed|\n| 3.    | Exhaustive Search Feature Selection | This notebook explains the concept of Exhaustive Search Feature Selection.| Completed |\n\n# Embedded Methods\n\n| S.No. |       Name        |                           About                                    |    Status    |\n| ----- | ----------------- | ------------------------------------------------------------------ | ------------ |\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanujdutt9%2Ffeature-selection-for-machine-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fanujdutt9%2Ffeature-selection-for-machine-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanujdutt9%2Ffeature-selection-for-machine-learning/lists"}