Final Project of Data Mining Course - Big Data Analysis

Predict_Credit_Card_DefaultPay

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• categories:
• machine learning
• date: "2020-01-24"
• title: Predict Credit Card Pay - Final Project of Data Mining Course - Big Data Analysis

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default of credit card clients Data Set Sources URL : https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients This research aimed at the case of customer default payments in Taiwan and compares the predictive accuracy of probability ,confusion matrix, AUC ( Area Under the curve). There are data mining methods I will use:

1. Linear Regression
2. Logistic Regression
3. kNN ( K-Nearest Neighbors)
4. SVM ( Support Vector Machine)
5. Naive Bayes
6. Linear Discriminan
7. Gradient Boosting Classifier
8. Decision Tree
9. Xgboost

Our Data contains : 3000 observation and 24 features/variables

Attribute Information

This research employed a binary variable, default payment (Yes = 1, No = 0), as the response variable. This study reviewed the literature and used the following 23 variables as explanatory variables: Attribute Description Value

• X1 Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit. Amount of the given credit (dollar)
• X2 Gender (1 = male; 2 = female).
• X3 Education (1 = graduate school; 2 = university; 3 = high school; 4 = others).
• X4 Marital status (1 = married; 2 = single; 3 = others).
• X5 Age (year)
• X6 History of past payment. Tracked the past monthly payment records (from April to September, 2005) as follows: X6 = the repayment status in September, 2005; The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months; . . .; 8 = payment delay for eight months; 9 = payment delay for nine months and above.
• X7 the repayment status in Agustus 2005
• X8 the repayment status in July, 2005
• X9 the repayment status in June, 2005
• X10 the repayment status in Mei, 2005
• X11 the repayment status in April, 2005.
• X12 amount of bill statement in September, 2005. Amount of bill statement (NT dollar).
• X13 amount of bill statement in August, 2005.
• X14 amount of bill statement in July, 2005.
• X15 amount of bill statement in June, 2005.
• X16 amount of bill statement in Mei, 2005.
• X17 amount of bill statement in April, 2005.
• X18 amount paid in September, 2005. X18-X23:Amount of previous payment (NT dollar).
• X19 amount paid in August, 2005.
• X20 amount paid in July, 2005.
• X21 amount paid in June, 2005.
• X22 amount paid in Mei, 2005.
• X23 amount paid in April, 2005.
• Y default payment next month Target Variable, default payment (Yes = 1, No = 0)