Machine Learning Tutorial Python : Logistic Regression | One Hot Encoding | SVM | Train & Test Data

Machine learning models work very well for dataset having only numbers. But how do we handle text information in dataset? Simple approach is to use interger or label encoding but when categorical variables are nominal, using simple label encoding can be problematic. One hot encoding is the technique that can help in this situation. In this tutorial, we will use pandas get_dummies method to create dummy variables that allows us to perform one hot encoding on given dataset. Alternatively we can use sklearn.preprocessing OneHotEncoder as well to create dummy variables.

sklearn.model_selection.train_test_split method is used in machine learning projects to split available dataset into training and test set. This way you can train and test on separate datasets. When you test your model using dataset that model didn't see during training phase, it will give you better idea on the accuracy of a model.

logistic regression is used for classification problems in machine learning. This tutorial will show you how to use sklearn logisticregression class to solve binary classification problem to predict if a customer would buy a life insurance. At the end we have an interesting exercise for you to solve.
Usually there are two types of machine learning problems (1) Linear regression where prediction value is continuous (2) Classification where predicted value is categorical. Logistic regression is used for classification problems mainly.

Support vector machine (SVM) is a popular classification algorithm. This tutorial covers some theory first and then goes over python coding to solve iris flower classification problem using svm and sklearn library. We also cover different parameters such as gamma, regularization and how to fine tune svm classifier using these parameters. Basically the way support vector machine works is it draws a hyper plane in n dimension space such that it maximizes the margin between classification groups.


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