{"id":16908973,"url":"https://github.com/saswatamcode/datascienceapi","last_synced_at":"2026-05-07T08:37:19.500Z","repository":{"id":40974308,"uuid":"225790405","full_name":"saswatamcode/DataScienceAPI","owner":"saswatamcode","description":"This is a RESTful API built using Flask and Scikit-Learn. 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It provides a host of Classification and Regression algorithms that can be used readily and returns results in the form of predictions, confusion matrices, accuracy scores and more.\n\n## Description\n- /rfclassification: Classification using Random Forest algorithm.\n- /rfregression: Regression using Random Forest algorithm.\n- /svmclassification: Classification using Support Vector Machines algorithm.\n- /knnclassification: Classification using K-Nearest Neighbor algorithm.\n- /dtclassification: Classification using Decision Trees algorithm.\n- /svmregression: Regression using Support Vector Machines algorithm.\n- /dtregression: Regression using Decision Trees algorithm.\n- /knnregression: Regression using K-Nearest Neighbor algorithm.\n- /gnbclassification: Classification using Naive Bayes(Gaussian) algorithm.\n- /bnbclassification: Classification using Naive Bayes(Bernoulli) algorithm.\n- /logisticregression: Classification using Logistic Regression algorithm.\n\n## To Run\n- Clone into repo\n- Type in `pip install` (preferably inside a virtual environment)\n- Then run `python3 main.py`\n- Use a REST client to make post requests to the Flask Server\n\nTwo sample datasets and the request format are included to test out the API.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaswatamcode%2Fdatascienceapi","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsaswatamcode%2Fdatascienceapi","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaswatamcode%2Fdatascienceapi/lists"}