This document presents a video presentation on using data analytics for loan prediction based on customer behavior. It explores a dataset from Kaggle containing information like income, age, experience, marital status, and risk flags to predict whether a consumer is likely to default on a loan. The presentation discusses collecting and exploring the data, identifying issues like a numeric class label that were addressed, and analyzing which attributes like risk flag and credit worthiness were most correlated to the class label and predictive of defaults. Attributes like marital status showed little impact. The goal is to provide insights into customer behavior for accurate loan prediction in the financial industry.

1. “LOAN PREDICTION BASED ON
CUSTOMER BEHAVIOR ”
Predict who possible Defaulters are for the Consumer
Loans Product
PRESENTED BY:
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2. DATAANALYTICS:
SECOND DELIVERABLE -
VIDEO PRESENTATION
• Introduction
• Name And Source Of Dataset
• Collection Of Dataset
• Purpose Of Each Column In Dataset
• Overview Of The Data
• Data Quality Problems And Its Solution
• Top Distinctive Categorical Attribute
• Top Distinctive Numerical Attribute
• Attribute Having No Impact

3. INTRODUCTION
• Loan prediction is a crucial aspect of the financial
industry.
• Data analytics can provide valuable insights into
customer behavior for accurate loan prediction.
• This presentation will explore the benefits and process of
using data analytics for loan prediction based on
customer behavior.

4. DATA EXPLORATION
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5. THE NAME AND SOURCE
OF DATASET
• Name: Loan Prediction Based on Customer Behavior: Predict
who possible Defaulters are for the Consumer Loans Product
• Source: https://www.kaggle.com/datasets/subhamjain/loan-
prediction-based-on-customer-behavior

6. COLLECTION OF
DATASET
• The dataset was created after collecting data about
demographics of user, professional experience,
ownerships, marital status, risk flag, area of residence,
and time in that residence. Based on this information, a
dataset made to predict whether the student is worthy
giving credit or not.

7. PURPOSE OF EACH
COLUMN IN DATASET
• Income – Income of the user
• Age – Age of the user
• Experience – Professional experience of the user in years
• Married – Whether married or single
• Ownership – Does the person own a car/house
• Risk Flag – Defaulted on a loan
• Current Job/ House Years:Years of experience in the current
job/ house

8. AN OVERVIEW OF THE
DATA
• We converted the .CSV file to .ARFF using Weka
itself
• Everything was done correctly.

9. DATA QUALITY
PROBLEMS AND ITS
SOLUTION
• The only data quality issue faced was that the
visualizations were black and white, and inaccurate.
• Later it was discovered that it was due to the class label
being numeric.
• To rectify this issue we applied a filter to the class label
(Unsupervised > Attributes> NumericToNominal -R last)
The issue was solved immediately

10. VISUALIZATION

11. TOP DISTINCTIVE
CATEGORICAL
ATTRIBUTE
• The top distinctive categorical attribute is: Risk Flag
• This attribute is highly correlated with the class label. It
is because it plays a important role in predicting
whether the student ever defaulted.

12. TOP DISTINCTIVE
NUMERICAL ATTRIBUTE
• The top distinctive numerical attribute is the credit
worthiness of the student.
• It plays a vital role in determining whether the student is
worthy enough to grant loan and at what interest rate.

13. ATTRIBUTE HAVING NO
IMPACT ON THE CLASS
LABEL
• The attribute that has no impact on the class label is
whether the student is married or single
• According to the visualization, there is minimum
correlation between them.

14. THE END