data.md

 ---
 title: Data
 ---
+# Data Description
 
+The Airline Dataset contains 15 features describing about passenger personal details, flight details, and flight performance. Below is a detailed description of each feature.
+
+# Feature Variables
+
+ 1. **Passenger ID:** _int64_
+    * Unique identifier for passengers.
+ 2. **First Name**: _string_
+    * The first name of the passenger.
+ 3. **Last Name**: _string_
+    * The last name of the passenger.
+ 4. **Gender**: _string_
+    * Values are categorical (Male, Female).
+    * Transformation: May be encoded as binary (e.g., Male = 0, Female = 1) or one-hot encoding for analysis.
+ 5. **Age**: _int64_
+    * Age of the passenger.
+    * Range: \[1, 90\]
+    * Min: 1 , Max: 90, Mean: 46.25
+ 6. **Nationality**: _string_
+    * The country of origin for the passenger.
+ 7. **Airport Name**: _string_
+    * Name of the originating or destination airport.
+ 8. **Airport Country Code**: _string_
+    * Country code of the airport (e.g., USA, CANADA, FRANCE etc).
+ 9. **Country Name**: _string_
+    * Full country name corresponding to the airport.
+10. **Airport Continent**: _string_
+    * Values include NAM (North America), EU (Europe), etc.
+    * Transformation: May be encoded as categorical numerical values or one-hot encoded.
+11. **Continents**: _string_
+    * Generalized continent grouping.
+    * Transformation: Similar to **Airport Continent**, encode numerically if necessary.
+12. **Departure Date**: _string_
+    * The date of the flight's departure.
+    * Transformation: Convert to a `datetime` format for time-based analysis.
+13. **Arrival Airport**: _string_
+    * Code of the destination airport.
+14. **Pilot Name**: _string_
+    * Name of the pilot for the flight.
+15. **Flight Status**: _string_
+    * Categorical values (On Time, Delayed).
+    * Transformation: Encode numerically for machine learning (e.g., On Time = 0, Delayed = 1).
+
+# Target Variables
+
+As per the the Airline dataset the target variables can Flight status.
+
+**Flight Status**: _Categorical -\> Binary_
+
+* The primary target variable, indicating whether the flight was on time or delayed.
+* It helps to understand how well the airline operates and what causes delays.
+* Transformation: Convert to binary values for classification models with only two possible outcomes. (e.g., On Time = 0, Delayed = 1).
+
+##### Application
+
+* **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. 
+
+# Data Transformation
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