A person’s creditworthiness is often associated (conversely) with the likelihood they may default on loans.
We’re giving you anonymized data on about 1000 loan applications, along with a certain set of attributes about the applicant itself, and whether they were considered high risk.
0 = Low credit risk i.e high chance of paying back the loan amount
1 = High credit risk i.e low chance of paying back the loan amount
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Dataset
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Dataset Description
The dataset has two files:
applicant.csv
: This file contains personal data about the (primary) applicant
- Unique ID:
applicant_id
(string) - Other fields:
- Primary_applicant_age_in_years (numeric)
- Gender (string)
- Marital_status (string)
- Number_of_dependents (numeric)
- Housing (string)
- Years_at_current_residence (numeric)
- Employment_status (string)
- Has_been_employed_for_at_least (string)
- Has_been_employed_for_at_most (string)
- Telephone (string)
- Foreign_worker (numeric)
- Savings_account_balance (string)
- Balance_in_existing_bank_account_(lower_limit_of_bucket) (string)
- Balance_in_existing_bank_account_(upper_limit_of_bucket) (string)
loan.csv
: This file contains data more specific to the loan application
- Target:
high_risk_application
(numeric) - Other fields:
- applicant_id (string)
- Months_loan_taken_for (numeric)
- Purpose (string)
- Principal_loan_amount (numeric)
- EMI_rate_in_percentage_of_disposable_income (numeric)
- Property (string)
- Has_coapplicant (numeric)
- Has_guarantor (numeric)
- Other_EMI_plans (string)
- Number_of_existing_loans_at_this_bank (numeric)
- Loan_history (string)
- Do the Exploratory Data Analysis & share the insights.
- How would you segment customers based on their risk (of default).
- Which of these segments / sub-segments would you propose be approved?
- For e.g. Would a person with critical credit history be more creditworthy? Are young people more creditworthy? Would a person with more credit accounts be more creditworthy?
- Tell us what your observations were on the data itself (completeness, skews).
Develop the ML model(s) to predict the credit risk(low or high) for a given applicant.
Business Constraint: Note that it is worse to state an applicant as a low credit risk when they are actually a high risk, than it is to state an applicant to be a high credit risk when they aren’t.
Provide the answers for the below points:
- Explain your intuition behind the features used for modeling.
- Are you creating new derived features? If yes explain the intuition behind them.
- Are there missing values? If yes how you plan to handle it.
- How categorical features are handled for modeling.
- Describe the features correlation using correlation matrix. Tell us about few correlated feature & share your understanding on why they are correlated.
- Do you plan to drop the correlated feature? If yes then how.
- Which ML algorithm you plan to use for modeling.
- Train two (at least) ML models to predict the credit risk & provide the confusion matrix for each model.
- How you will select the hyperparameters for models trained in above step.
- Which metric(s) you will choose to select between the set of models.
- Explain how you will export the trained models & deploy it for prediction in production.