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📢Day 10/100: Class Imbalance Challenges
Class imbalance is a persistent issue in fraud detection and credit scoring. 🚨

In my dataset:

Fraudulent transactions are rare (<5%), making prediction tricky.
Techniques like SMOTE (Synthetic Minority Oversampling Technique) helped balance the dataset.
💡 Key Insight: Balancing the data improved model precision for rare classes like fraud detection.

💡 Question: What other methods do you use to address class imbalance without oversampling?

#DataChallenges #FraudDetection #CreditScoring #FintechInnovation



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📢Day 10/100: Class Imbalance Challenges
Class imbalance is a persistent issue in fraud detection and credit scoring. 🚨

In my dataset:

Fraudulent transactions are rare (<5%), making prediction tricky.
Techniques like SMOTE (Synthetic Minority Oversampling Technique) helped balance the dataset.
💡 Key Insight: Balancing the data improved model precision for rare classes like fraud detection.

💡 Question: What other methods do you use to address class imbalance without oversampling?

#DataChallenges #FraudDetection #CreditScoring #FintechInnovation

BY Epython Lab




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