The Airline Dataset contains 15 features describing about passenger personal details, flight details, and flight performance. Below is a detailed description of each feature.
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@@ -63,6 +60,19 @@ As per the the Airline dataset the target variables can Flight status.
***Operational Analysis:** Helps identify patterns in delays based on flight details like departure time, airport, or pilot name.
***Predictive Modelling:** Make it easier/possible to create models based on predicts delays, helps to improve customer comfort, planning and experience.
The factors that can influence flight status can be the more busy airport the more delay it can be, flights during peak hours, weather conditions.
The factors that can influence flight status can be the more busy airport the more delay it can be, flights during peak hours, weather conditions.
# Data Transformation
There were no transformations appear to have been performed on the dataset.
Yes, there might be several opportunities for data transformation in our dataset. Here are some suggestion:
1. Departure Date: - Extract components like Year, Month, Day of the week and convert to a datetime format for analysis.
2. Flight Status: - Encode as numerical values. (example., 0 = Delayed, 1 = On time, 2 = Cancelled).
3.**`First Name`** and **`Last Name`** could be dropped unless you are performing text or name-based analysis.
4. Merge **`Airport Name`** and **`Airport Country Code`** into a single feature for uniqueness.
Here are some suggestions for Data transformation if there might be possible in our Airline Dataset.
