{"id":20651471,"url":"https://github.com/nikhiljsk/generic_regression_algo","last_synced_at":"2026-05-01T20:31:42.600Z","repository":{"id":139245683,"uuid":"150895808","full_name":"nikhiljsk/Generic_regression_algo","owner":"nikhiljsk","description":"A Python template to evaluate different regression models on a dataset. Includes metrics to cross-compare models on the data. 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The number of features influencing the result are reduced for ease of understanding. The code produces the ouput of different algorithms including:\n* Linear Regression\n* Polynomial Regression\n* Support Vector Regression\n* Decision Tree Regression\n* Random Forest Regression\n\nIn the end, it produces the graph which contains error rate of each algorithm implying the most efficient algorithm.\n\n## Libraries Required\n* Numpy\n* Pandas\n* Matplotlib\n* Seaborn\n* SciKit-Learn\n\n## Running the program\nOnce all the required libraries are installed, the program provides a CLI to work with and you are good to go.\n\n## Running the program on your own dataset\nYou can use Generic_template.py to run the program on your own dataset, and get the error rates, so as to choose the best regression algorithm. \n\nCheers :)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnikhiljsk%2Fgeneric_regression_algo","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnikhiljsk%2Fgeneric_regression_algo","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnikhiljsk%2Fgeneric_regression_algo/lists"}