📢Day 9/100: Feature Engineering Deep Dive
Feature engineering is where raw data turns into actionable insights! 🛠
In my credit scoring project, key features include:
1️⃣ Recency, Frequency, Monetary (RFM): Critical for understanding customer behavior.
2️⃣ Fraud indicators: High-value transactions flagged based on outlier analysis.
3️⃣ Categorical encodings: Using Weight of Evidence (WoE) to transform qualitative data like product categories.
💡 Takeaway: Good features are the foundation of any successful model. They ensure the patterns we observe are meaningful and actionable.
💡 Discussion point: What’s your go-to method for handling highly skewed data in financial datasets?
#FeatureEngineering #DataScience #CreditScoring #FintechEthiopia
Feature engineering is where raw data turns into actionable insights! 🛠
In my credit scoring project, key features include:
1️⃣ Recency, Frequency, Monetary (RFM): Critical for understanding customer behavior.
2️⃣ Fraud indicators: High-value transactions flagged based on outlier analysis.
3️⃣ Categorical encodings: Using Weight of Evidence (WoE) to transform qualitative data like product categories.
💡 Takeaway: Good features are the foundation of any successful model. They ensure the patterns we observe are meaningful and actionable.
💡 Discussion point: What’s your go-to method for handling highly skewed data in financial datasets?
#FeatureEngineering #DataScience #CreditScoring #FintechEthiopia