British_Airlines_Customer_Reviews_Analysis

Getting Started

Web scraping and analysis

This Jupyter notebook includes some code to get you started with web scraping. We will use a package called BeautifulSoup to collect the data from the web. Once you've collected your data and saved it into a local .csv file you should start with your analysis.

Scraping Review Data from Skytrax

If you visit [https://www.airlinequality.com] you can see that there is a lot of data there. For this task, we are only interested in reviews related to British Airways and the Airline itself.

If you navigate to this link: [https://www.airlinequality.com/airline-reviews/british-airways] you will see this data. Now, we can use Python and BeautifulSoup to collect all the links to the reviews and then to collect the text data on each of the individual review links.

First of all, we scrap Review data from Skytrax.

Then we scrap the Route data and then the Seat Type, some Numerical data and Score data from the website.

Finally, we save it as BA_DataSet.csv.

Analyze

Organizing Data

• First of all, we separate Approval_Status and Review.
• We remove unwanted parts from Approval_Status.

Data Analysis 1 - Review Analysis - Good/Bad/Neutral Discrimination

We analyze sentiment using the TextBlob library and look at its distribution in the graph.

Data Analysis 2 - Route Analysis - From/To

• According to the to we divide the routes into From and To.
• According to via we separate Transfer data.
• To avoid complexity in the From and To columns, we reduce data that may represent the same thing to one data.
• We look at which data is redundant and support it with a graph.

Data Analysis 3 - Seat Type Analysis

Graphing how much the Seat_Type data is.

Data Analysis 4 - Correlation Analysis

We examine the correlation of Numerical data with Score. Then we also examine the correlation between them.

Data Analysis 5 - Seat_Type and Review_Type Charts

• Visualization of the average Score data according to Seat_Type and Review_Type data with a graph.
• Examine how many of which data according to Review_Type and Seat_Type.
• Then examining its relationship with other Numerical data.

Saving The Clean DataSet

Finally, we save it as BA_Clean_DataSet.csv.

Score Prediction