A diagnostic pipeline for verifying statistical assumptions in supervised learning models.
Content: Machine Learning (Overview & Process Flow), Linear Regression concept, Assumption checking, Linearity, Multi co-linearity, Auto correlation, Outliers, Null value treatment, EDA, Splitting the test and train data, Applying LinearRegression on train data, Predicting y_pred based on test data, Constructing a dataframe, Evaluating RMSE and Rsq
Add a description, image, and links to the assumption-checking topic page so that developers can more easily learn about it.
To associate your repository with the assumption-checking topic, visit your repo's landing page and select "manage topics."