This contains many basic projects of the Topic from Supervised learning part of Machine Leaning Algorithms.
It deals with the basic how to import a certain a library for that algoritm from the sklearn and to further fit the data and predict the best possible output.
1.These codes could be run in any i-pyhton console.
2.Code is written in python v3.6 and sklearn v0.20.1 . So versions of atleast these or higher are recommended.
In every project we follow certain steps:
1.Import the library and the data.
2.Divide the data into the training ,Cross Validation and Testing set.
3.Fit the model only using the training data.
4.Check for the perfect hyperparameters using the cross validation set.
5.Predict the output from the best estimated model using the testing data.
6.Check the accuracy score of the predicted model.