Aviation Analytics Course

CRISP-DM is a widely-used project management methodology for machine learning (ML) projects. It covers the entire ML process and is designed to be flexible and adaptable to various types of projects. The methodology includes steps such as business understanding, data understanding, data preparation, modelling, evaluation and deployment.

Exploratory Data Analysis (EDA) and preprocessing are important steps in analyzing aviation data. EDA is used to understand the characteristics of the data and identify patterns, while preprocessing is used to clean, transform and prepare the data for further analysis. These steps are crucial to ensure accurate and reliable results in aviation data analysis.

Airline customer segmentation is the process of dividing customers into distinct groups based on demographics, behaviour, and preferences. This helps airlines to understand and target specific market segments, create personalized marketing campaigns and provide tailored service to improve customer loyalty and increase revenue. Segmentation using K means algorithm

Machine Learning can be used to predict the likelihood of a fight being cancelled by analysing factors such as past cancellations, fighter injuries, and weather conditions. This can help event organizers plan for contingencies and make more informed decisions about event scheduling.

Linear regression can be used to predict flight ticket prices by analysing factors such as time of booking, destination, and historical prices etc. This can help passengers make more informed decisions about when to book their flights.

Machine learning can be used to predict customer satisfaction on flights by analysing factors such as flight delays, seating arrangements, and in-flight amenities. This can help airlines improve customer satisfaction and make data-driven decisions to enhance the overall flight experience for customers.

Artificial Neural Networks (ANN) or Multi-layer perceptron (MLP) can be used to predict flight delays by analysing factors such as past flight records, weather conditions, and maintenance issues etc.. This can help airlines to improve flight scheduling and reduce delays.

Natural Language Processing (NLP) can be used to analyse customer sentiment from reviews of airline data. This involves cleaning and preprocessing text data, and then using techniques such as sentiment analysis and topic modeling to identify patterns and understand customer opinions and perceptions of the airline. This can be used to improve customer service and enhance the overall customer experience