Why this matters
Raw records rarely arrive in a form a model can use well. A list of repayment dates says little until you turn it into "days since last payment" or "number of late payments this year". The columns you build, called features, usually matter more than which algorithm you pick. Adverts for modelling roles name feature engineering directly, and it is where your knowledge of the people and places behind the data pays off.
Build features that mean something
Good features come from understanding the situation. Ask what a sensible field officer would look at.
- Time since: days since last repayment, last transaction, or last clinic visit.
- Counts and rates over a window: transactions in the last 30 days, share of payments made late this season.
- Ratios: loan size per acre farmed, spending relative to the person's own usual level.
- Place: district, distance to the nearest market or clinic, rainfall zone.
- Season: month, or whether a date falls in the lean season before harvest.
Here is a made up example for farmers on an input credit scheme, where farmers is a pandas table (imported as pd) with one row per farmer: