Thera Bank Personal Loan

(Use PYTHON and give me answer with codes and Markdown / File should be in CSV)

Context:

This case is about a bank (Thera Bank) whose management wants to explore ways of converting

its liability customers to personal loan customers (while retaining them as depositors). A

campaign that the bank ran last year for liability customers showed a healthy conversion rate of

over 9% success. This has encouraged the retail marketing department to devise campaigns

with better target marketing to increase the success ratio with a minimal budget.

Question No: 1

EDA: Study the data distribution in each attribute and target variable, share your findings (2

1. Number of unique in each column?
2. Number of people with zero mortgage?
3. Number of people with zero credit card spending per month?
4. Value counts of all categorical columns.
5. Univariate and Bivariate
6. Get data model ready

Question No: 2

Split the data into training and test set in the ratio of 70:30 respectively

Question No: 3

Use Logistic Regression model to predict whether the customer will take personal loan or not. Print all the metrics related for evaluating the model performance.

Question No: 4

Give your reasoning on how can the model perform better?