Airline Passengers Satisfaction Classification

Content

• Project Description
• Objective
• Workflow
• Organization
• Links

Project Description

Customer analytics: Data Analysis and Visualizations using the Airline Passenger Satisfaction dataset from Kaggle.

Content of the dataset

• Socio-demographics data from customers.
• Service attributes satisfaction grades given by the customers.
• Satisfaction/Dissatisfaction of customers.

Objective

• Predict dissatisfaction: predictive analysis using sklearn library.
• Customers Clustering: classification analysis using sklearn library.

Workflow

1. Data search, import and setting the libraries Pandas, Numpy, Matplotlib, Seaborn and SKlearn.
2. Data cleaning Clean data and prepare for analysis.
3. Data Analysis and Visualization.
4. Dissatisfaction Prediction Analysis: dataset preparation, normalization of attributes, one hot encoding, feature selection and applying model.
5. Customer Clustering: same steps as before, but with one difference: features transformation with Principal Component Analysis (PCA). Thus, clustering customers in 5 different customer segments.
6. Presentation: construction and oral presentation to the students of Ironhack Data Cohort.

Organization

• Repository "https://github.com/Sherlyn02/Airline-Passenger-Satisfaction" : you may find the main Python code as well as the data used. You may find the Google Slides in the 'Links' section below or by clicking on the cover photo on this document.

PS.: individual project.

Links

Here you may find the relevant links for the repository, the main code and the final presentation slides.

GitHub Repository: airline-passenger-satisfaction

Jupyter Notebook: Airline Passenger Satisfaction