Predicting Credit Card Approvals

Detailed Project Link

How We Approached the Project

• Importation of the data and libraries
• Preprocessed the data and carried out exploratory data analysis
  • Checked for missing values and duplicates
  • Endcoded categorical columns with LabelEncoder.
  • Visualized the distributtion with a Histplot
  • Checked correlation between variables
• Data Analysis
  • Built a model to pridict credit cards approval
  • Improved the model with hyperparameter tuning (using GridSearchCV)
  • Decile Analysis
• Conclusion and recommendations

Decile Analysis

The decile analysis shows that the top 10% of customers have about 80% probability that their credit cards would be approved. Customers in deciles 1-6 have more than 50% chance that their credit cards would be approved. Customers in deciles 7-10 have less than 50 % chance of getting their credit cards approved.

General Findings

• More than 50% of credit card applications get approved.
• Males apply for credit cards more than females.
• The most credit card applicants are between age 20 and 40. This age group also has more credit card approvals than the other age groups.
• Applicants aged above 60 years are the least to apply for credit cards and the ones who are less likely to have their credit cards approved.
• The smaller a customer’s debt, the higher the chances of a credit card approval.
• The model has about 88% accuracy of predicting a credit card approval.
• Top 10% of customers have about 80% probability that their credit cards would be approved.
• Customers in deciles 1-6 have more than 50% chance that their credit cards would be approved.
• Customers in deciles 7-10 have less than 50 % chance of getting their credit cards approved.

Recommendations

• Approve credit cards of customers in deciles 1-6.
• Do not approve the credit cards of customers in deciles 7-10.

Libraries Used

• Pandas
• Numpy
• matplotlib
• Scikit-learn
• Scikit-plot
• Seaborn