Presentation & Findings on Above report

1. Identifying Default of Credit Card Payments
- Vikas Virani(s3715555)
- Salina Bharthu(s3736867)
 Banks dealing with the risk of potential of customer to repay credit card bills
 Study aims at providing model to predict likelihood of default payment
 Classifying probability of default payment using KNN and Decision tree algorithms.
Data Description:
 Data source: UCI machine learning repository( https://archive.ics.uci.edu/ml/machine-learning-databases/00350/)
 Dataset contains 30000 observations and 24 variables.
 Holds customers’ personal details such as(age, education, gender, marital status)
 Holds financial details such as(balance limit, previous payment status, previous bill amounts, previous amount paid, default payment status)
Data Preparation:
 Checking data against missing values, impossible values and outliers.
- No missing value present
- Outliers are removed using Z-score approach
- Impossible values replaced by nearest possible value
 Assigning appropriate datatype to categorical variables.
 Display summary of nominal variables and numerical variables.

2. Data Exploration:
 Visualizing individual variables to find the trends
 Visualizing pair of variables to explore hypothesis
- Does personal details (such as age, sex, education, marital status) or balance limit or past payment status impacts
the chances of default?
- Age: Relatively similar chances of default in all age groups
- Sex: Female customers are more probable to default as compared to male.
- Education: Higher chances of default for university graduate as compared to school and high school graduate respectively.
- Marital status: Similar chances of default for married, single and others.
- Balance limit: The bill amount is slightly higher for non-default cards as compared to default cards.
- Previous Payment Status: The chances of default are increased when there is a delay in previous payments for even 1 month.
- Do spending habits vary for different age groups, sex or marital status?
- Age : People below the age of 24 tend to spend less money overall. Whereas, spending habits are equally distributed among other age groups, people
from 55- 65 tend to spend more.
- Sex: Spending habits does not vary in gender.
- Marital status: Married people tend to spend more than others
- Do Payment delays are dependent on Age, Gender or Marital Status of the Person?
- Age: payment status follows similar trend in all age groups
- Gender: Women are having higher proportion of chances of payment delay than men.
- Marital status: Higher number of single card holders having payment delays as compared to married and others.

3. Feature Selection:
 Using MinMaxScaler() to set all features in the same scale
 Applying F1 Score technique to select the best features of dataset
Data Modelling:
 Splitting up data into training and test dataset.
 Applying KNN and Decision tree classifiers.
 Applying Resampling technique to balance the dataset in terms of target feature values.
 Using GridSearchCV to find best suitable combination of attribute values of classifier.
 Validating model using confusion matrix, Classification report, accuracy score and error rate.
Conclusion:
 To effectively classify unseen data for Default Payment Status, Decision tree classifier works better.
Classification Algorithm Training- test data
proportion
Accuracy score Error rate
K nearest neighbour 80% - 20% 0.742 0.258
K nearest neighbour 60% - 40% 0.726 0.274
K nearest neighbour 50% - 50% 0.732 0.268
Decision Tree 80% - 20% 0.800 0.200
Decision Tree 60% - 40% 0.803 0.197
Decision Tree 50% - 50% 0.789 0.211