1. Find a dataset with min.5 - max. 10 features, and one output. 2. First, the correlation tests must be applied for all input-input and input-output sets, by Python codes. Then, decide which features to be included in the dataset, by showing the related outputs. 3. Apply the supervised learning methods given below, that are emphasized in the lesson, by Python. Show the performance metric results and related graphics. Compare the method results and choose the most appropriate one for the dataset. a) Multiple Linear Regression b) Polinomial Regression c) Logistic Regression d) K-NN e) Naive Bayes f) Decision Trees g) Random Forest h) Support Vector Machine,

Fig: 1