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@@ -47,7 +47,7 @@ I checked the service rating scales (like Inflight wifi service) to make sure al
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  I used the IQR (Interquartile Range) method to find outliers in columns like Age, Flight Distance, and delay times.
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  I calculated the percentage of outliers for each feature to understand how many extreme values exist in the data.
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  #### Data Exploration: Answering Key Research Questions through Visualization
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- ###### Following the detection of outliers in flight distance, how extreme is their distribution and what impact might they have on the model's scaling?
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  ![image](https://cdn-uploads.huggingface.co/production/uploads/69c79aa8f856b118f80df631/oIXebKJ0KqMjckAlGjYn-.png)
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  This boxplot displays the distribution of flight distances and identifies extreme outliers that could distort the model's data scaling.
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  It serves as visual evidence for the capping strategy needed to ensure data quality and better performance in future modeling.
@@ -62,11 +62,11 @@ While these extreme values can skew numerical predictions in regression, they ar
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  ![image](https://cdn-uploads.huggingface.co/production/uploads/69c79aa8f856b118f80df631/wgnKWFRPS--OgH--lLnEK.png)
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  The plot shows that while most ratings are concentrated between 4 and 5, it visually confirms the presence of '0' values across various service categories.
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- ###### What is the correlation between departure and arrival delays, and how do extreme outliers reflect unusual flight patterns?
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  ![image](https://cdn-uploads.huggingface.co/production/uploads/69c79aa8f856b118f80df631/qJy6FKjaVxlonbAhShNcf.png)
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  This scatter plot shows a strong positive correlation between departure and arrival delays, while highlighting how extreme outliers deviate from the main cluster.
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- ###### What is the correlation between departure and arrival delays, and how do cleaning the extreme outliers reflect the flight patterns?
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  ![image](https://cdn-uploads.huggingface.co/production/uploads/69c79aa8f856b118f80df631/udUMbiqphRopYggAj7llT.png)
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  After cleaning the data, the scatter plot now displays a much clearer and more reliable linear relationship between the two types of delays.
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  Removing the extreme anomalies allows us to visualize the core data patterns that will be used for our predictive modeling.
 
47
  I used the IQR (Interquartile Range) method to find outliers in columns like Age, Flight Distance, and delay times.
48
  I calculated the percentage of outliers for each feature to understand how many extreme values exist in the data.
49
  #### Data Exploration: Answering Key Research Questions through Visualization
50
+ #### Following the detection of outliers in flight distance, how extreme is their distribution and what impact might they have on the model's scaling?
51
  ![image](https://cdn-uploads.huggingface.co/production/uploads/69c79aa8f856b118f80df631/oIXebKJ0KqMjckAlGjYn-.png)
52
  This boxplot displays the distribution of flight distances and identifies extreme outliers that could distort the model's data scaling.
53
  It serves as visual evidence for the capping strategy needed to ensure data quality and better performance in future modeling.
 
62
  ![image](https://cdn-uploads.huggingface.co/production/uploads/69c79aa8f856b118f80df631/wgnKWFRPS--OgH--lLnEK.png)
63
  The plot shows that while most ratings are concentrated between 4 and 5, it visually confirms the presence of '0' values across various service categories.
64
 
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+ ##### What is the correlation between departure and arrival delays, and how do extreme outliers reflect unusual flight patterns?
66
  ![image](https://cdn-uploads.huggingface.co/production/uploads/69c79aa8f856b118f80df631/qJy6FKjaVxlonbAhShNcf.png)
67
  This scatter plot shows a strong positive correlation between departure and arrival delays, while highlighting how extreme outliers deviate from the main cluster.
68
 
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+ ##### What is the correlation between departure and arrival delays, and how do cleaning the extreme outliers reflect the flight patterns?
70
  ![image](https://cdn-uploads.huggingface.co/production/uploads/69c79aa8f856b118f80df631/udUMbiqphRopYggAj7llT.png)
71
  After cleaning the data, the scatter plot now displays a much clearer and more reliable linear relationship between the two types of delays.
72
  Removing the extreme anomalies allows us to visualize the core data patterns that will be used for our predictive modeling.