https://github.com/soumyaco/hotel-price-data-analysis

Data analysis on Indian hotels price. A beginners guide to data analysis.
https://github.com/soumyaco/hotel-price-data-analysis

data-analysis-python data-science data-visualization matplotlib seaborn-plots

Last synced: about 1 month ago
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Data analysis on Indian hotels price. A beginners guide to data analysis.

• Host: GitHub
• URL: https://github.com/soumyaco/hotel-price-data-analysis
• Owner: SoumyaCO
• License: mit
• Created: 2023-08-21T21:09:11.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-08-22T14:26:45.000Z (over 1 year ago)
• Last Synced: 2024-10-25T11:50:00.522Z (3 months ago)
• Topics: data-analysis-python, data-science, data-visualization, matplotlib, seaborn-plots
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/code/soumyadipbhat/data-analysis-with-hotel-price-data
• Size: 643 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
Indan Hotels Price Analysis



Analysis of Hotel Price Data from the MakeMyTrip website for major cities like Bangalore, Chennai, Hyderabad, Delhi, Kolkata


The dataset is available on kaggle you can take a look at the dataset: 🔗[here](https://www.kaggle.com/datasets/andrewgeorgeissac/hotel-price-data-of-cities-in-india-makemytrip). I've done some analysis on this data such as 

> * hotel qualities

> * average people choice which kind of hotels

> * High rating actually increase number of customers or not

> * Average hotel price

> * Finding out important landmarks based on the hotel price and no of customers

and many more.

## 💿 Data Card:

The data consists of these columns:

* Hotel Name

* Rating

* Rating Description

* Reviews

* Star rating

* Location

* Nearest Landmark

* Distance to the Landmark

* Price

* Tax

also Price is excluding the Tax. So total price will be Price + Tax

## 🧑🏻‍💻 Approach:

* Cleaning the data - Renaming some columns, dropping unnecessary columns etc.

* Visualizing the missing values (with `seaborn` and `missingno` library)

* Plotting correlation between the data [like Price and Tax has a strong correlation etc.]

* Plotting data individually [like Places vs Price, Reviews vs Price etc.]

* Conclusion about the analysis.

## 🤝 Collaboration: 

Anybody interested in this and have some suggestion to improve or some more analysis on this dataset and want to contribute can give a **PR**. Anykind of contribution will be highly appriciated. Thank you.

👉 In the notebook I've provided detailed codes and concepts. If you like it please give a star ⭐️

## 🧑🏻‍💻 My Profiles:

> * [🔗 LinkedIn](https://www.linkedin.com/in/soumyadip-bhattacharjya-993974234/)

> * [🔗 Kaggle](https://www.kaggle.com/soumyadipbhat)