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README.md
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In this final analytical stage, I evaluated the three trained classifiers using Confusion Matrices to understand their prediction patterns and error types.
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### Model Performance Analysis:
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Logistic Regression: This model served as a strong baseline, achieving the highest number of True Negatives (10,380). It is highly reliable at identifying on-time flights but struggle with a significant number of False Negatives (3,079), meaning it often misses actual delays.
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Decision Tree: While it captured the highest number of True Positives (5,989), it suffered from the highest rate of False Positives (2,974). This indicates that the single tree is prone to "over-detecting" delays, leading to many false alarms.
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Random Forest: This model provided the most balanced performance. It maintained a high count of True Negatives (9,596) while successfully identifying 5,902 delayed flights with significantly fewer false alarms than the Decision Tree.
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In this final analytical stage, I evaluated the three trained classifiers using Confusion Matrices to understand their prediction patterns and error types.
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| 150 |
### Model Performance Analysis:
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| 151 |
Logistic Regression: This model served as a strong baseline, achieving the highest number of True Negatives (10,380). It is highly reliable at identifying on-time flights but struggle with a significant number of False Negatives (3,079), meaning it often misses actual delays.
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Decision Tree: While it captured the highest number of True Positives (5,989), it suffered from the highest rate of False Positives (2,974). This indicates that the single tree is prone to "over-detecting" delays, leading to many false alarms.
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Random Forest: This model provided the most balanced performance. It maintained a high count of True Negatives (9,596) while successfully identifying 5,902 delayed flights with significantly fewer false alarms than the Decision Tree.
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