Homework - (the main goal is to practice with basic ml application...

Homework - (the main goal is to practice with BASIC ML application steps):
**File format:** IPython Notebook (ipynb) with supplemental data (if present). Use your dataset OR continue to work with the Customer Churning dataset. 1.State the problem you want to solve using ML algorithms. Is it classification or regression problem? 2.Provide basic EDA (summary statistics, number of missing values, scatterplots, corrmat, etc.). 3.Select features for your model. Argue. 4.Do Feature Engineering (missing data imputation, feature scaling, handle categorical features, take logs, etc.). Provide couple of sentences why it is needed for your analysis. 5.Select two algorithms you want to compare on your dataset. 6.Build models, Cross-Validate, Select model based on scoring metric OR use Grid Search to model evaluation and selection. 7.Complement your code with comments!