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viral_predictor

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@@ -102,8 +102,9 @@ We repeated the comparison process, pitting **Logistic Regression** (Baseline) a
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  ### What Drives Virality? (Interpretation)
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  We analyzed which features the model found most important.
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- ![Feature Importance](image_60a2b4.png)
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- *Above: ROC Curve (Left) and Feature Importance Plot (Right).*
 
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  **💡 Key Insights:**
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  * **#1 Predictor (kw_avg_avg):** The historical performance of keywords is the strongest predictor. If a topic was popular in the past, it is likely to be popular again. This suggests a "Caching Effect" in audience interest.
 
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  ### What Drives Virality? (Interpretation)
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  We analyzed which features the model found most important.
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+ ![image](https://cdn-uploads.huggingface.co/production/uploads/67dfcd96d01eab4618a66f78/nI0qJkf_gsC7ckR-vJAs8.png)
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  **💡 Key Insights:**
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  * **#1 Predictor (kw_avg_avg):** The historical performance of keywords is the strongest predictor. If a topic was popular in the past, it is likely to be popular again. This suggests a "Caching Effect" in audience interest.