- To conduct a thorough exploratory data analysis (EDA) and deep analysis of a comprehensive dataset containing basic customer details and extensive credit-related information. The aim is to create new, informative features, calculate a hypothetical credit score, and uncover meaningful patterns, anomalies, and insights within the data.
- Deep driving into bank details and credit data, creating valuable features, a hypothetical credit score, and uncovering hidden patterns. This involves thorough EDA, strategic feature engineering, model-driven score calculation, and insightful analysis that reveals factors influencing creditworthiness and guides potential risk mitigation strategies.
| Column Name | Description |
|---|---|
| ID | Represents a unique identification of an entry |
| Customer_ID | Represents a unique identification of a person |
| Month | Represents the month of the year |
| Name | Represents the name of a person |
| Age | Represents the age of the person |
| SSN | Represents the social security number of a person |
| Occupation | Represents the occupation of the person |
| Annual_Income | Represents the annual income of the person |
| Monthly_Inhand_Salary | Represents the monthly base salary of a person |
| Num_Bank_Accounts | Represents the number of bank accounts a person holds |
| Num_Credit_Card | Represents the number of other credit cards held by a person |
| Interest_Rate | Represents the interest rate on credit card |
| Num_of_Loan | Represents the number of loans taken from the bank |
| Type_of_Loan | Represents the types of loan taken by a person |
| Delay_from_due_date | Represents the average number of days delayed from the payment date |
| Num_of_Delayed_Payment | Represents the average number of payments delayed by a person |
| Changed_Credit_Limit | Represents the percentage change in credit card limit |
| Num_Credit_Inquiries | Represents the number of credit card inquiries |
| Credit_Mix | Represents the classification of the mix of credits |
| Outstanding_Debt | Represents the remaining debt to be paid (in USD) |
| Credit_Utilization_Ratio | Represents the utilization ratio of credit card |
| Credit_History_Age | Represents the age of credit history of the person |
| Payment_of_Min_Amount | Represents whether only the minimum amount was paid by the person |
| Total_EMI_per_month | Represents the monthly EMI payments (in USD) |
| Amount_invested_monthly | Represents the monthly amount invested by the customer (in USD) |
| Payment_Behaviour | Represents the payment behavior of the customer |
| Monthly_Balance | Represents the monthly balance amount of the customer (in USD) |
- Exploratory Data Analysis (EDA)
- Feature Engineering
- Hypothetical Credit Score Calculation
- Analysis and Insights
- You can access the full Python analysis on Google Colab using the following link: View the notebook
A detailed analysis report is available in the following PDF file: View Report.