N26: Exploring user transaction data to uncover patterns, insights, and key questions. Pandas, Scikit-Learn, Matplotlib
Given a dataset of transactions for 10,000 users and their demographic profiles, define regression models for predicting the total expenses and total income of a user.
| File | Description |
|---|---|
transactions_data_training.csv | Transaction records for ~10k users |
user_profile.csv | Demographic and account information per user |
transaction_types.csv | Dictionary explaining transaction_type codes and their flow direction |
mcc_group_definition.csv | Dictionary explaining mcc_group merchant category codes |
Define, train, and evaluate predictive models that take the provided data as input. You may want to split the data into training, testing, and validation sets according to your discretion. Do not use external data for this project. You may use any algorithm of your choice or compare multiple models.
Make sure that the solution reflects your entire thought process — it is more important how the code is structured than the final metrics. You are expected to spend no more than 3 hours working on this project.
See the full interview round by round, with questions for each stage.