Predicting Flight Delays

Flight delays have become an important subject and problem for air transportation systems all over the world. The aviation industry is continuing to suffer from economic losses associated with flight delays all the time. According to data from the Bureau of Transportation Statistics (BTS) of the United States, more than 20% of U.S. flights were delayed in 2018. These flight delays have a severe economic impact in the U.S. that is equivalent to 40.7 billion dollars per year. Passengers suffer a loss of time, missed business opportunities or leisure activities, and airlines attempting to make up for delays leads to extra fuel consumption and a larger adverse environmental impact. In order to alleviate the negative economic and environmental impacts caused by unexpected flight delays, and balance increasing flight demand with growing flight delays, an accurate prediction of flight delays in airports is needed.

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Airport delays may result from airlines operations, air traffic congestion, weather, air traffic management initiatives, etc. Most of the reasons are stochastic phenomena which are difficult to predict timely and accurately.

The goal of this project is to develop a computational model for predicting the delays based on data for flights extracted from Kaggle.

The first phase is getting data from Kaggle and stores it into PostgreSQL. Second phase is data cleaning. After loading data into database, I cleaned the data mainly depend on business needs. After cleaning all data, next phase is feature engineering, where you create features for machine learning model from raw data. Fourth phase is exploratory data analysis. In this phase I create graphics to understand data. Fifth phase is model analysis, where I applied machine learning algorithms on dataset.

#data-science #data-visualization #postgresql #roc #machine-learning

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Predicting Flight Delays
Ismael  Stark

Ismael Stark


Flight Delay Prediction

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

Ian  Robinson

Ian Robinson


Predictive Modeling in Data Science

Predictive modeling is an integral tool used in the data science world — learn the five primary predictive models and how to use them properly.

Predictive modeling in data science is used to answer the question “What is going to happen in the future, based on known past behaviors?” Modeling is an essential part of data science, and it is mainly divided into predictive and preventive modeling. Predictive modeling, also known as predictive analytics, is the process of using data and statistical algorithms to predict outcomes with data models. Anything from sports outcomes, television ratings to technological advances, and corporate economies can be predicted using these models.

Top 5 Predictive Models

  1. Classification Model: It is the simplest of all predictive analytics models. It puts data in categories based on its historical data. Classification models are best to answer “yes or no” types of questions.
  2. Clustering Model: This model groups data points into separate groups, based on similar behavior.
  3. **Forecast Model: **One of the most widely used predictive analytics models. It deals with metric value prediction, and this model can be applied wherever historical numerical data is available.
  4. Outliers Model: This model, as the name suggests, is oriented around exceptional data entries within a dataset. It can identify exceptional figures either by themselves or in concurrence with other numbers and categories.
  5. Time Series Model: This predictive model consists of a series of data points captured, using time as the input limit. It uses the data from previous years to develop a numerical metric and predicts the next three to six weeks of data using that metric.

#big data #data science #predictive analytics #predictive analysis #predictive modeling #predictive models

Otho  Hagenes

Otho Hagenes


Top Five Artificial Intelligence Predictions For 2021

As AI becomes more ubiquitous, it’s also become more autonomous — able to act on its own without human supervision. This demonstrates progress, but it also introduces concerns around control over AI. The AI Arms Race has driven organizations everywhere to deliver the most sophisticated algorithms around, but this can come at a price, ignoring cultural and ethical values that are critical to responsible AI. Here are five predictions on what we should expect to see in AI in 2021:

  1. Something’s going to give around AI governance
  2. Most consumers will continue to be sceptical of AI
  3. Digital transformation (DX) finds its moment
  4. Organizations will increasingly push AI to the edge
  5. ModelOps will become the “go-to” approach for AI deployment.

#opinions #2021 ai predictions #ai predictions for 2021 #artificial intelligence predictions #five artificial intelligence predictions for 2021

How do airlines handle flight delays

Have you ever wondered why flights get delayed? Why do planes have to circle in the sky before landing? How do airlines act if a pilot or a crew member is missing? The answers to these and many more flight disruption questions are in our video

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#flightdelays #delays #slots #slottimes #aviation #padding #arrival #departure #airlineindustry #travelworks #traveltechnology #travelindustry

Music in video:

Favorite by Alexander Nakarada

Around The Globe by Alexander Nakarada

Upbeat Piano by Rafael Krux

Story Of My Life by AlekSound

Positive Advertising by Rafael Krux

Stock footage provided by Videvo, downloaded from

#airlines #flight delays

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