Assignment: Naive Bayes Classifier

Your task for this assignment is to implement a Naive Bayes classifier to identify potential customers who have a higher probability of purchasing a loan.

Instructions

** A complete guide has been provided in the Colab notebook below to help you complete the assignment.**

  • Use this bank_loan dataset to train and test your model.
  • Create a Colab notebook and write your code in it.
  • Without using any high-level libraries like sklearn or tensorflow, implement the Naive Bayes classifier to predict the potential customers who have a higher probability of accepting the personal loan offer.
  • The dataset contains 12 featurs and 1 target variable ("Personal Loan").
    • age : Customer's age in completed years
    • experience : years of professional experience
    • income : Annual income of the customer
    • zip_code : Home Address ZIP code.
    • family : Family size of the customer
    • ccavg : Avg. spending on credit cards per month
    • education : Education Level (Undergrad, Graduate, Advanced/Professional)
    • mortgage : Value of house mortgage if any.
    • personal_loan : Did this customer accept the personal loan offered in the last campaign?
    • 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?
  • Implement basic data exploration techniques to understand the dataset.

Guide

A complete guide, including a video, has been provided in the Colab notebook below to help you complete the assignment.

Submission

  • Submit your notebook link via Gradescope here