Project Description
Aviation and flight performance analysis is critical to optimizing flight operations, increasing customer satisfaction and ensuring operational efficiency. In this project, we will evaluate flight performance, delay reasons and customer satisfaction by analyzing flight information, weather data, airport information and flight feedback data. Our goal is to help airlines and airport managers improve flight operations and make strategic decisions.
Project Usage Areas
This project has several uses for airlines, airport managers and data analysts:
Flight Performance Analysis: Improving performance and identifying causes of delays by analyzing flight information.
Weather Impact: Evaluating impacts on flight operations by analyzing weather data.
Customer Satisfaction: Identifying ways to increase customer satisfaction by analyzing flight feedback.
Operational Efficiency: Increasing operational efficiency by analyzing flight and airport data.
Strategic Decisions: Developing and improving airline and airport management strategies using data analysis.
Dataset Description
The data set to be used in this project includes aviation and flight performance data. The dataset consists of four main files in total:
Flight Information (flight_data)
FlightID: Flight ID
Airline: Airline
FlightDate: Flight date
DepartureTime: Departure time
ArrivalTime: Arrival time
Origin: Departure airport
Destination: Arrival airport
Duration: Flight duration (hours)
Delay: Delay time (minutes)
Distance: Distance (kilometers)
Weather Data (weather_data)
Date: Date
Airport: Airport
Temperature: Temperature (°C)
Visibility: Visibility distance (km)
WindSpeed: Wind speed (km/h)
Precipitation: Precipitation amount (mm)
Airport Information (airport_data)
AirportID: Airport ID
AirportName: Airport name
city: city
Country: Country
Latitude: Latitude
Longitude: Longitude
Flight Feedback (flight_feedback)
FeedbackID: Feedback ID
FlightID: Flight ID
FeedbackDate: Feedback date
Rating: Evaluation (between 1-5)
Comments: Comments
There are various dirty data problems in this dataset, such as missing data, outlier data, and wrong data type. This is an ideal data set to experience data cleaning and processing processes commonly encountered in real life.
Student Benefits
This project provides many benefits for students:
Data Manipulation: Students develop skills in examining, cleaning, and analyzing data sets.
Using Pandas: They learn to use the data processing and analysis methods of the Pandas library effectively.
Data Cleaning: They gain skills in cleaning missing data, outliers and incorrect data types.
Business Intelligence: By analyzing data sets, they improve their ability to evaluate flight performance and customer satisfaction and make strategic decisions.
Reporting: Provides skills to effectively report and present analysis results.
Real Life Applications: Provides practical information about data problems and analysis processes encountered in real life.
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