Project-1

Data-Driven Customer Review Analysis for British Airways || Python

British Airways

In the vast landscape of data, my recent expedition involved embarking on a journey through British Airways customer reviews. Armed with Python's formidable libraries—BeautifulSoup, Requests, and Pandas—I set out to uncover the sentiments and nuances within a treasure trove of a thousand customer testimonials.

The Quest:

The quest's objective was clear: to glean insights from the collective voice of British Airways passengers, encapsulated in reviews scattered across the digital realm. This data analyst adventure unfolded in three distinct chapters.

Chapter 1: Web Scraping Odyssey:

In the first chapter, I harnessed the power of web scraping. Armed with Python, I summoned BeautifulSoup and Pandas as my companions. Together, we ventured into the expanse of British Airways reviews, meticulously collecting and parsing HTML content. The result? A trove of a thousand reviews transformed into a structured Pandas DataFrame, ready for the next leg of the journey.

Chapter 2: Data Cleaning Chronicles:

As any seasoned explorer would attest, the road to insights is often paved with data cleaning challenges. With Pandas as my guide, I navigated through the DataFrame, exploring its dimensions, handling null values, and bidding farewell to irrelevant columns. The reviews underwent a metamorphosis, shedding unwanted patterns through the mystic arts of the re module. Sentiment analysis, powered by TextBlob, added a layer of emotional depth to each review, and categorization painted a vivid picture of passenger experiences.

Chapter 3: Analytical Odyssey:

The final chapter, a grand finale, involved the analytical unraveling of the British Airways narrative. Reviews were grouped by category, sentiments were labeled, and percentages danced on the canvas of positivity and negativity. A pivot table emerged, a mosaic of sentiment counts for each category, and a Pandas DataFrame transformed this mosaic into a structured masterpiece. Categories were sorted by the echoes of passenger voices, and the story was etched into a CSV file—cleaned_reviews_analysis.csv—an artifact of the analytical odyssey.

Key Insights:

Customer Feedback Analysis

  • Service Category: Unraveling like a magician's act, "Service" steals the spotlight with a dazzling 45.70% of total reviews. It's not just a flight; it's a performance worth applauding!

  • Staff: Representing a modest 8.20% of reviews, the staff category suggests room for improvement. Perhaps a sprinkle of humor in in-flight announcements could turn those frowns upside down.

Sentiment Analysis

  • The majority of reviews (64.50%) are like a chorus of cheerful birds, singing praises for British Airways. It seems like passengers are more "yay" than "nay"!

  • Constituting 34.40% of reviews, the negative sentiment is like that one cloud on an otherwise sunny day. A sprinkle of humor could turn those clouds into silver linings.

  • So, it's safe to say, British Airways has more fans than a blockbuster movie premiering at 30,000 feet.

Additional Revelations, Because Why Not?

Customer Applause

  • High-fives all around for the "Service" category. It's not just good; it's "call-your-mom-and-tell-her-about-it" good.

  • Positive vibes also detected in the "Food" and "Other" categories. It's official; in-flight snacks are the unsung heroes of air travel!

Areas for Improvement (Cue Dramatic Music)

  • "Comfort" is like that one character in a sitcom who needs a glow-up. Let's give it a makeover, shall we?

  • Negative reviews are like grumpy cats on the internet - impossible to ignore. Time to turn those frowns upside down, BA style!

The Outcome:

This project unraveled the sentiments and stories within British Airways reviews. Through web scraping prowess, data cleaning acumen, and analytical insights, I not only explored the data landscape but transformed it into a narrative rich with valuable insights.