Design-Statistical-Models

This repository, contains the Jupyter notebooks and datasets companions to the OpenClassrooms course: Design Statistical Models

Part I

We start with the core concepts required to build linear regression models

• Linearity
• Correlation
• Hypothesis testing

Part II

We move on to univariate and multivariate linear regression. We start with hands-on applications to standard datasets and follow up with the underlying theoretical basis.

• Univariate and multivariate linear regression
• The 5 assumptions of linear regression
• The mathematical basis for Linear regression

Part III

We expand the framework of linear regression for classification, handling categorical variables and polynomial regression.

• Logistic regression
• Dealing with categorical variables
• Polynomial regression

Part IV

In the last part we move from statistical modeling to a predictive analytics approach and address overfitting, cross validation and classification metrics.

• Predicting with linear regression
• Classification metrics and model selection

The datasets are available in the /data folder.