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harshad317/zindi-July-Study-Jam-Series-African-Credit-Scoring-Challenge

Domain:

socioeconomic

Record type:

paper
Creator:
har
Host:
# 7th Place — Zindi African Credit Scoring Challenge This repository contains my **7th-place solution** for Zindi's July Study Jam Series: African Credit Scoring Challenge. It retrains the complete pipeline from the four official competition files and reproduces the accepted submission `candidate85_policy_exact_same_loan_plus_hpoGhanaType2_p075.csv`. | Result | Value | |---|---:| | Final leaderboard position | **7th** | | Recorded Zindi F1 | **0.826405867** | | Submission rows | **18,594** | | Predicted positives | **910** | | Submission SHA-256 | `7d5daa6d6bd1a79e097ab23a36ac3a6f5f1ca21ebda6326004bc5b511a5f08c9` | **Quick navigation:** Competition · Metric · Run the solution · Understand the pipeline · Review features and models · Inspect reproducibility ## Competition overview The official Zindi competition asked participants to help a private African asset manager assess credit risk. Financial institutions need reliable default predictions to approve suitable borrowers, price risk, control losses, and expand lending into new markets. ### Problem statement The task was to build a robust, generalisable binary-classification model that predicts whether a loan will default from anonymised customer, loan, lender, repayment, and economic information: - `target = 1` means that the loan defaults. - `target = 0` means that the loan does not default. - Each submitted row must contain the official `ID` and a binary `target`. Although the business problem is expressed as estimating default likelihood, the competition submission and F1 evaluation use the final binary decision. For the top-ranked solutions, the broader goal also included turning model outputs into understandable risk bands and a scalable credit-scoring function. ### Why the problem is challenging - **Severe class imbalance:** only 1,258 of 68,654 Train rows are defaults (1.83%). A model that predicts every loan as non-default would be highly accurate but operationally useless and would receive …

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text classification