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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.

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Design Statistical Models on OpenClassrooms

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