When the new files will be added, corresponding description will also be added with file name and DDMMYY.
base of these programs are machine learning codes influenced from Muller's Machine Learning with Python book. Later on many extra techniques are implemented. \
PCA_Muller.py 190818: Principal component analysis example with breast cancer data-set. Detailed description of this code is discussed in here https://towardsdatascience.com/dive-into-pca-principal-component-analysis-with-python-43ded13ead21 . After this un-supervised machine learning technique, we will move to some very well known supervised machine learning methods like KNearestNeighbor and LinearRegression and so on. \
270918: RidgeandLin.py, LassoandLin.py: Lasso and Ridge regression examples: From coefficient shrinakge in Ridge to feature selection in Lasso are shown in the code. The concepts and discussion of the results are shown here (https://towardsdatascience.com/ridge-and-lasso-regression-a-complete-guide-with-python-scikit-learn-e20e34bcbf0b). \
081018: bank.csv, data set of portuguese company selling products to random customer over a phone call. Detailed description are available here http://archive.ics.uci.edu/ml/datasets/Bank+Marketing
161018: gender_purchase.csv, data-set of two columns describing customers buying a product depending on gender.
111118: winequality-red.csv, red wine data set, where the output is the quality column which ranges from 0 to 10. This output is unbalanced as most of them are normal. So be careful!!
121118: pipelineWine.py, this program contains a simple example of applying pipeline and gridsearchCV together using the red wine data. More description can be found here https://towardsdatascience.com/a-simple-example-of-pipeline-in-machine-learning-with-scikit-learn-e726ffbb6976