https://github.com/ankhanhquach/universal_banking_loan_prediction_knn

This machine learning initiative seeks to leverage the k-Nearest Neighbors (k-NN) classification algorithm to predict whether a Universal Bank will accept a personal loan offer.
https://github.com/ankhanhquach/universal_banking_loan_prediction_knn

k-nn machine-learning r

Last synced: 27 days ago
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This machine learning initiative seeks to leverage the k-Nearest Neighbors (k-NN) classification algorithm to predict whether a Universal Bank will accept a personal loan offer.

• Host: GitHub
• URL: https://github.com/ankhanhquach/universal_banking_loan_prediction_knn
• Owner: ankhanhquach
• Created: 2023-10-18T10:40:33.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-10-18T10:58:47.000Z (over 1 year ago)
• Last Synced: 2024-11-06T07:17:00.764Z (3 months ago)
• Topics: k-nn, machine-learning, r
• Language: R
• Homepage:
• Size: 205 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Universal Bank Loan Prediction: k-NN Model

This repository contains the machine learning project that employs k-NN classification to predict if a Universal Bank customer will accept a personal loan offer. 

## Table of Contents

1. [Submission Instructions](#submission-instructions)

2. [Data Description](#data-description)

3. [Scenario](#scenario)

4. [Tasks](#tasks)

## Submission Instructions

- Answers should be written as text or comments within the R codes.

- Compile R scripts to html/word/pdf for submission.

  - Shortcut: `Ctrl + Shift + K` then select "html".

- Submit the html file via Canvas before the deadline.

## Data Description

- Dataset: `Universalbank.csv`.

- Contains data on 5000 bank customers.

- Includes demographics, bank relationship details, and past loan campaign responses.

- Key metric: Only 9.6% accepted the last personal loan offer.

## Scenario

Universal Bank seeks to convert its liability customers to personal loan customers. With a previous campaign seeing a 9% conversion rate, the bank aims to optimize its strategy using k-NN predictions for better-targeted campaigns.

> "Do the right thing; and do it right." - Master Yeoda.

## Tasks

1. **Data Handling**

    - Import and split data: 60% training and 40% validation.

    a. Check variable data types.

    b. Set up kNN with relevant variables.

2. **k-NN Modelling**

    - Exclude ID and ZIP code predictors.

    - Factorize categorical variables.

    - Test with k values: 3, 5, and 7. Select the best k.

3. **Model Assessment**

    - Predict on the validation set using optimal k.

    a. Visualize with an ROC curve.

    b. Gauge model efficacy.

4. **Sample Prediction**

    - Predict loan acceptance for a specific customer profile provided.