HomeWork #1 EDX GTx: ISYE6501x - Introduction to Analytics Modeling Mónica Rojas May 17, 2020, Georgia Tech,

HomeWork #1 EDX GTx: ISYE6501x - Introduction to Analytics Modeling Mónica Rojas May 17, 2020, Georgia Tech, Document Content and Description Below HomeWork #1 EDX GTx: ISYE6501x - Introduction to Analytics Modeling Mónica Rojas May 17, 2020 Table of Contents Results........................................................................... ..................................................................................1 Question 2.1 .............................................................................................................................................1 Question 2.2 .............................................................................................................................................2 Part 1......................................................................................................................................................2 Part 2......................................................................................................................................................8 Part 3......................................................................................................................................................9 Question 3.1 .......................................................................................................................................... 11 Part a ................................................................................................................................................... 11 Part b................................................................................................................................................... 12 Results Question 2.1 Describe a situation or problem from your job, everyday life, current events, etc., for which a classification model would be appropriate. List some (up to 5) predictors that you might use. In my case, a classification model would be appropriate to determine which clients will close their accounts. I work for a bank where it is important to have our clients happy with our service and avoid churn. Attracting new customers is more expensive than keeping existing ones. Some of the predictors I found are: - Banking predictors: - Account balance - Age as a customer - Products quantity - Recent Complaints- Demographic predictors: - Profession - Marital status - Type (Companny or not) - Gender Question 2.2 The files credit_card_ (without headers) and credit_card_ (with headers) contain a dataset with 654 data points, 6 continuous and 4 binary predictor variables. It has anonymized credit card applications with a binary response variable (last column) indicating if the application was positive or negative. The dataset is the “Credit Approval Data Set” from the UCI Machine Learning Repository (