Data Summary:
The dataset named Bank.xls comprises information related to 5000 clients. It encompasses details about customer demographics (such as age, income, etc.), the nature of the customer's interactions with the bank (including mortgage, securities account, etc.), and the customer's response to the most recent personal loan campaign (referred to as "Personal Loan"). Among these 5000 customers
Field: Finance and Banking
Learning Achievements:
Conducting Exploratory Data Analysis
Performing Data Cleansing
Creating Data Visualizations
Preprocessing data for model training purposes
Training a classification model and utilizing it for predictions
Evaluating the performance of the model
Objective:
The classification goal is to predict the likelihood of a liability customer buying personal loans which means we have to build a model which will be used to predict which customer
will most likely to accept the offer for personal loan, based on the specific relationship with the bank across various features given in the dataset. Here I will be using the
Supervised Learning methods to predict which model is best for this problem amongst Logistic Regresssion, K-Nearest Neighbors(KNN) and Naive Bayes Algorigthm.
List of Columns:
●I D: ID of the customer
● Age: Age of the customer
● Gender: M for Male, F for Female and O for Others
● Experience: Amount of work experience in years
● Income: Amount of annual income (in thousands)
● Home Ownership: Home Owner, Rent and Home Mortgage.
● Zipcode: Postal code in which the client lives
● Family: Number of family members
● CCAvg: Average monthly spending with the credit card (in thousands)
● Education: Education level (1: bachelor's degree, 2: master's degree, 3:
advanced/professional degree)
● Mortgage: Value of home mortgage, if any (in thousands)
● Securities Account: Does the customer have a securities account with the bank?
● CD Account: Does the customer have a certificate of deposit account (CD) with the
bank?
● Online: Does the customer use the internet banking facilities?
● CreditCard: Does the customer use a credit card issued by the bank?
● Personal Loan: Did this customer accept the personal loan offered in the last campaign?
(Target Variable)