Delay is one of the most remembered performance indicators of any transportation system. Notably, commercial aviation players understand delay as the period by which a flight is late or postponed. Thus, a delay may be represented by the difference between scheduled and real times of departure or arrival of a plane. Country regulator authorities have a multitude of indicators related to tolerance thresholds for flight delays. Indeed, flight delay is an essential subject in the context of air transportation systems. In 2013, 36% of flights delayed by more than five minutes in Europe, 31.1% of flights delayed by more than 15 minutes in the United States. This indicates how relevant this indicator is and how it affects no matter the scale of airline meshes. To better understand the entire flight ecosystems, vast volumes of data from commercial aviation is collected every moment and stored in databases. Submerged in this massive amount of data produced by sensors and IoT, analysts and data scientists are intensifying their computational and data management skills to extract useful information from each datum. In this context, the procedure of comprehending the domain, managing data and applying a model is known as Data Science, a trend in solving challenging problems related to Big Data. In this project, we’ve performed an extensive data analysis in order to extract the important attributes/factors that are responsible for the delay of flight. Also, there will other factors that may influence the delay of the flight such as climate, natural calamities, pandemic, or technical issues, etc. in the airplane which has not been considered in this project as this factors varying depend on the location and such problems occurring have very less frequency.

Flight delays are quite frequent (19% of the US domestic flights arrive more than 15 minutes late), and are a major source of frustration and cost for the passengers. As we will see, some flights are more frequently delayed than others, and there is an interest in providing this information to travellers.
Flight prediction is crucial during the decision-making process for all players of commercial aviation. Moreover, the development of accurate prediction models for flight delays became cumbersome due to the complexity of air transportation system, the number of methods for prediction, and the deluge of flight data. Based on data, we would like to analyse what are the major cause for flight delays and assign a probability on whether a particular flight will be delayed.

The objective of the project is to perform analysis of the historic flight data to gain valuable insights and build a predictive model to predict whether a flight will be delayed or not given a set of flight characteristics. Questions to be answered post analysis: • Does the month of flight have any impact on flight delays? • Flights to which destination have seen the most delays? • Which day of the week sees the least and most flight delays? • Which time of day is most suitable for preventing flight delays? • Which airline has the most number of flights delayed? • What are the primary causes for flight delays? The objective of the predictive model is to build a model to predict whether a flight will be delayed or not based on certain characteristics of the flight. Such a model may help both passengers as well as airline companies to predict future delays and minimize them.

Dataset obtained from Kaggle:
https://www.kaggle.com/lampubhutia/nyc-flight-delay

• Python
• HTML
• CSS
• Bootstrap
• Numpy
• Pandas
• Matplotlib
• Seaborn
• Flask

Setup the virtual environment and turn it on
>> source Flight-Delay-Prediction/bin/activate (For Mac and Linux)
>> .\Flight-Delay-Prediction\Scripts\activate (For Windows)

Run the script
>> python app.py

Contributors

• Himani Joshi
  
• Adinath Kankekar
  
• Devvrat Mungekar