Project

Flight delay prediction

Project (M.S., Computer Science)--California State University, Sacramento, 2018.

Nowadays, the aviation industry plays a crucial role in the world's transportation sector, and a lot of businesses rely on various airlines to connect them with other parts of the world. But, extreme weather conditions may directly affect the airline services by means of flight delays.
 
 To solve this issue, accurately predicting these flight delays allows passengers to be well prepared for the deterrent caused to their journey and enables airlines to respond to the potential causes of the flight delays in advance to diminish the negative impact.
 
 The purpose of this project is to look at the approaches used to build models for predicting flight delays that occur due to bad weather conditions. 
 
 In the first part of the project, we look at using Python based Logistic Regression along with Support Vector Machine and then plugging the dataset into our classifier for results. 
 
 In the second part of the project, we primarily focus on gathering a dataset from Twitter, breaking the dataset down and identifying relevant attributes. Upon examining the results, we compare the results with other models such as Random Forest Classifier and derive the best classifier to solve the problem.

Nowadays, the aviation industry plays a crucial role in the world's transportation sector, and a lot of businesses rely on various airlines to connect them with other parts of the world. But, extreme weather conditions may directly affect the airline services by means of flight delays. To solve this issue, accurately predicting these flight delays allows passengers to be well prepared for the deterrent caused to their journey and enables airlines to respond to the potential causes of the flight delays in advance to diminish the negative impact. The purpose of this project is to look at the approaches used to build models for predicting flight delays that occur due to bad weather conditions. In the first part of the project, we look at using Python based Logistic Regression along with Support Vector Machine and then plugging the dataset into our classifier for results. In the second part of the project, we primarily focus on gathering a dataset from Twitter, breaking the dataset down and identifying relevant attributes. Upon examining the results, we compare the results with other models such as Random Forest Classifier and derive the best classifier to solve the problem.

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