Overview: Gamdevi Bank is a relatively young bank growing rapidly in terms of overall customer acquisition. The majority of these customers are liability customers (depositors) with varying sizes of relationship with the bank. The customer base of asset customers (borrowers) is quite small, and the bank is interested in expanding this base rapidly to bring in more loan business. In particular, it 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 smarter campaigns with better target marketing. The goal is to predict whether a new customer will accept a loan offer. This will serve as the basis for the design of a new campaign. Data: The file Gamdevi Bank.csv contains data on 5000 customers. The data include customer demographic information (age, income, etc.), the customer's relationship with the bank (mortgage, securities account, etc.), and the customer response to the last personal loan campaign (Personal Loan). Among these 5000 customers, only 480 (= 9.6%) accepted the personal loan that was offered to them in the earlier campaign. Partition the data into training (75%) and validation (25%) sets in your analysis using random_state = 1. Variable Description ID Customer ID Age Customer's age in completed years Experience #years of professional experience Income Annual income of the customer ($000) Zip_Code Home Address Zip Code Family Family size of the customer CCAvg Avg. spending on credit cards per month ($000) Education Education Level. 1: Undergrad; 2: Graduate; 3: Advanced/Professional Mortgage Value of house mortgage if any. ($000) Securities_Account Does the customer have a securities account with the bank? CD_Account Does the customer have a certificate of deposit (CD) account with the bank? Online Does the customer use internet banking facilities? CreditCard Does the customer use a credit card issued by Gamdevi Bank? Personal Loan (Target) Did this customer accept the personal loan offered in the last campaign? Project Questions 1.) Import the Gamdevi Bank.csv into Python and libraries youll need for your analysis (may vary by technique) 2.) Data Exploration a. Review the data (describe, dtypes) b. Are there missing values? c. Do any of the variables look skewed or have entry concerns? (Plots, Frequencies) d. Apply data manipulation techniques that you deem appropriate (Examples: outliers, binning, transformation, imputation) e. Do you have any concerns regarding the dependent variable? f. Note: you may have to convert Personal_Loan to a category in your code 3.) Design a model(s) to predict whether customers will respond to the accuracy campaign a. Explain why the model was chosen b. What data preparation steps were used to estimate the model (variable adjustments) this may vary by model and relates to 2c above c. What is the accuracy of the model? Precision? Recall? d. If you ran more than one model explain which model you would choose to implement and why