Introduction
Flight Delay Prediction is one of the most talked-about projects in Kaggle. In this article, I am going to explain how did I manage to perform some analysis on the entire Flight Delay Prediction dataset. After that, I performed some preprocessing steps such as cleaning the data, replacing the null values, and then performing normalization wherever needed. Later the data was split into train and test sets and we build a Decision Tree model. We obtained around 99.9% of accuracy with the model. Once the model was built we created a static page using HTML to obtain the details from the user and at the same time, we used the model trained to obtain the result of whether the flight will be delayed or not based on the input features. We used the Flask to integrate the static pages with the model to display the user with the result of Flight Delay Prediction.
Importing the Libraries
We begin the analysis by importing the necessary libraries for building the model.

#flask #python #flight-delay-prediction #decision-tree-classifier #programming