Week_14

Week 14 - Multiple Linear Regression and Logistic Regression

Reading: https://www.w3schools.com/python/python_ml_multiple_regression.asp https://scikit-learn.org/stable/modules/preprocessing.html https://christophm.github.io/interpretable-ml-book/logistic.html

Recursive feature elimination: https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html https://machinelearningmastery.com/rfe-feature-selection-in-python/ https://towardsdatascience.com/feature-selection-with-pandas-e3690ad8504b

Preprocessing: https://scikit-learn.org/stable/modules/preprocessing.html

Merging one to many dataframes: https://jakevdp.github.io/PythonDataScienceHandbook/03.07-merge-and-join.html

Optional Reading: https://campus.datacamp.com/courses/introduction-to-regression-with-statsmodels-in-python/simple-linear-regression-modeling?ex=1

Python statistics: https://data-flair.training/blogs/python-statistics/

Read section 4.1 on Linear Regression https://christophm.github.io/interpretable-ml-book/limo.html

multiple linear regression: https://www.investopedia.com/terms/m/mlr.asp

logistic regressioin: https://www.datacamp.com/community/tutorials/understanding-logistic-regression-python

Performance metrics: https://blog.exsilio.com/all/accuracy-precision-recall-f1-score-interpretation-of-performance-measures/ https://muthu.co/understanding-the-classification-report-in-sklearn/

Parameter Tuning: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html

https://www.analyticssteps.com/blogs/l2-and-l1-regularization-machine-learning

https://machinelearningmastery.com/hyperparameters-for-classification-machine-learning-algorithms/