Sign In

Eda Submission

Exploratory data analysis - Hotel's Customer reviews


Exploratory Data Analysis (EDA) is the process of exploring, investigating and gathering meaningful insights and nuggets using different kind of statistical measures and visualizations. The objective of EDA is to develop an understanding of data by uncovering trends, relationships and patterns.

When it comes to the requirement of statistical knowledge, visulaization technique and data analysis tools like Numpy, Pandas, Matplotlib, etc. we categories it as an art. When there is reqirement of asking interesting questions to guide the investigation for generating meaningful insight we call it a science. So it is a mixture of both art and science.

How to Run the Code

The best way to learn the material is to execute the code and experiment with it yourself. This tutorial is an executable Jupyter notebook. You can run this tutorial and experiment with the code examples in a couple of ways: using free online resources (recommended) or on your computer.

Option 1: Running using free online resources (1-click, recommended)

The easiest way to start executing the code is to click the Run button at the top of this page and select Run on Binder. You can also select "Run on Colab" or "Run on Kaggle", but you'll need to create an account on Google Colab or Kaggle to use these platforms.

Option 2: Running on your computer locally

To run the code on your computer locally, you'll need to set up Python, download the notebook and install the required libraries. We recommend using the Conda distribution of Python. Click the Run button at the top of this page, select the Run Locally option, and follow the instructions.

Project Outline

  • Download and read the dataset

  • Data Processing & Cleaning with Pandas

  • Download a second dataset

  • Exploratory Analysis and Visualization

  • Asking and Answering Questions

  • References and Future Work


The data was scraped from All data in the file is publicly available to everyone already. Please be noted that data is originally owned by

This dataset contains 515,000 customer reviews and scoring of 1493 luxury hotels across Europe. Meanwhile, the geographical location of hotels are also provided for further analysis.

I have created visualizations (scatter plots, bar and pie charts, geo heatmaps, etc.) using Seaborn & Plotly

The csv file contains 17 fields. The description of each field is as below:

  • Hotel_Address: Address of hotel.
  • Review_Date: Date when reviewer posted the corresponding review.
  • Average_Score: Average Score of the hotel, calculated based on the latest comment in the last year.
  • Hotel_Name: Name of Hotel
  • Reviewer_Nationality: Nationality of Reviewer
  • Negative_Review: Negative Review the reviewer gave to the hotel. If the reviewer does not give the negative review, then it should be: 'No Negative'
  • ReviewTotalNegativeWordCounts: Total number of words in the negative review.
  • Positive_Review: Positive Review the reviewer gave to the hotel. If the reviewer does not give the negative review, then it should be: 'No Positive'
  • ReviewTotalPositiveWordCounts: Total number of words in the positive review.
  • Reviewer_Score: Score the reviewer has given to the hotel, based on his/her experience
  • TotalNumberofReviewsReviewerHasGiven: Number of Reviews the reviewers has given in the past.
  • TotalNumberof_Reviews: Total number of valid reviews the hotel has.
  • Tags: Tags reviewer gave the hotel.
  • dayssincereview: Duration between the review date and scrape date.
  • AdditionalNumberof_Scoring: There are also some guests who just made a scoring on the service rather than a review. This number indicates how many valid scores without review in there.
  • lat: Latitude of the hotel
  • lng: longtitude of the hotel

Some additional columns where created during the analysis to simplify it

Installing libraries and datasets

Francesco palmieri6 months ago