{"id":20448518,"url":"https://github.com/amajji/multi-class-classification","last_synced_at":"2025-09-24T17:30:59.037Z","repository":{"id":133033665,"uuid":"487098325","full_name":"amajji/Multi-class-classification","owner":"amajji","description":"Deployment of a classification model on a webapp using FLASK for the backend and html/CSS/JS for frontend","archived":false,"fork":false,"pushed_at":"2023-04-21T13:26:00.000Z","size":24207,"stargazers_count":4,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-11-15T10:48:21.648Z","etag":null,"topics":["analyse-data","app","classification","data","flask","flask-application","imbala","imbalanced-classes","imbalanced-classification","imbalanced-data","machine-learning","machine-learning-algorithms","preprocessing","webapp","webapplication"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/amajji.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-04-29T20:11:11.000Z","updated_at":"2023-06-13T04:49:49.000Z","dependencies_parsed_at":null,"dependency_job_id":"fde78fda-9127-40ec-9952-37d0f1facb8e","html_url":"https://github.com/amajji/Multi-class-classification","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/amajji%2FMulti-class-classification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amajji%2FMulti-class-classification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amajji%2FMulti-class-classification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amajji%2FMulti-class-classification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/amajji","download_url":"https://codeload.github.com/amajji/Multi-class-classification/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":234103256,"owners_count":18780212,"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":["analyse-data","app","classification","data","flask","flask-application","imbala","imbalanced-classes","imbalanced-classification","imbalanced-data","machine-learning","machine-learning-algorithms","preprocessing","webapp","webapplication"],"created_at":"2024-11-15T10:35:28.991Z","updated_at":"2025-09-24T17:30:56.566Z","avatar_url":"https://github.com/amajji.png","language":"Jupyter Notebook","readme":"# Multiclass classification.\nData scientist | [Anass MAJJI](https://www.linkedin.com/in/anass-majji-729773157/)\n***\n\n## :monocle_face: Description\n- This project aims to implement a multi-class classification model. we have four classes with a minority class (less than 1%), \nso we are in the case of unbalanced classes. We used regularization methods in order to penalize the errors made on this minority class.\nThe model is trained to predict around 4 different class. \u003c/br\u003e\n\n \n\n## :rocket: Repository Structure\nThe repository contains the following files \u0026 directories:\n- **Dataset directory:** It contains a data pre-processing notebook where the train.csv file is used for training \nthe model. Il contains also the predictions of test.csv dataframe.\n- **model_weights:** It contains all the weights of the models : one-hot-encoder, target encoder, random forest model.\n\n- **App directory:** Code for the web application that was developed for the model deployment. It contains Flask API code for the Back-End,\nand HTML/CSS/Javascript code for the Front-End.\n\n\n\n![](last_gif.gif)\n\n## :chart_with_upwards_trend: Performance \u0026 results\n\n- The test dataset contains **25 000 samples**. Each sample contains many features, and its corresponding label.\n\n- The model used for this multi-class classification task is a **Random Forest** model.\n\n- The metric used to measure the model's performance is **F1-score**. After testing the model, I obtained a test F1-score of **72 %**\n\n\n\n\n---\n## :mailbox_closed: Contact\nFor any information, feedback or questions, please [contact me][anass-email]\n\n\n\n\n\n[anass-email]: mailto:anassmajji34@gmail.com\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famajji%2Fmulti-class-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Famajji%2Fmulti-class-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famajji%2Fmulti-class-classification/lists"}