📢Day 8/100: Sketching My Credit Scoring Workflow
Today, I outlined the workflow for my credit scoring model. Here’s what it looks like:
1️⃣ Data collection: Leveraging transaction histories, behavioral metrics, and alternative data sources.
2️⃣ Feature engineering: Creating features like transaction recency, frequency, and value tailored to BNPL behavior.
3️⃣ Model selection: Comparing Gradient Boosting, Random Forest, and Logistic Regression.
4️⃣ Evaluation: Balancing precision, recall, and ROC-AUC to ensure the model is reliable in the Ethiopian context.
💡 Tips needed: What’s your go-to feature engineering strategy for financial datasets?
#AI #CreditScoring #ModelDevelopment #BNPL #DataScienceEthiopia
Today, I outlined the workflow for my credit scoring model. Here’s what it looks like:
1️⃣ Data collection: Leveraging transaction histories, behavioral metrics, and alternative data sources.
2️⃣ Feature engineering: Creating features like transaction recency, frequency, and value tailored to BNPL behavior.
3️⃣ Model selection: Comparing Gradient Boosting, Random Forest, and Logistic Regression.
4️⃣ Evaluation: Balancing precision, recall, and ROC-AUC to ensure the model is reliable in the Ethiopian context.
💡 Tips needed: What’s your go-to feature engineering strategy for financial datasets?
#AI #CreditScoring #ModelDevelopment #BNPL #DataScienceEthiopia