https://github.com/007tickooayush/eda-airbnb-project

Exploratory Data Analysis on Airbnb Bookings in 2018.
https://github.com/007tickooayush/eda-airbnb-project

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Exploratory Data Analysis on Airbnb Bookings in 2018.

• Host: GitHub
• URL: https://github.com/007tickooayush/eda-airbnb-project
• Owner: 007tickooayush
• Created: 2022-12-25T13:23:19.000Z (over 2 years ago)
• Default Branch: master
• Last Pushed: 2023-01-15T09:07:56.000Z (over 2 years ago)
• Last Synced: 2025-02-01T01:37:13.475Z (4 months ago)
• Language: Jupyter Notebook
• Size: 146 KB
• Stars: 0
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# EDA-Airbnb-Project

## **Project Summary -**

Since 2008 guests and hosts aave used Airbnb to expand n travelling possibilities and present a more unique, personalized way of experiencing the world. Today, Airbnb became one ofa kind service that is used and recognized by the whole world. Data Analysis on millions of listings provided thorough Airbnb is a crucial factor for the company. These millions of listings generate a lot o data - data that can be analysed and used for security, business decisions, understanding of customers' and providers' (hosts) behaviour and performance on the platform, guiding marketing initiatives, implementation of innovative additional services and much more.

### **Perform analysis and explore key findings about the data, like -**

* The relation between the price and neighbourhood group.

* The neighbourhood with most traffic (in terms of bookings).

* Preference of customers on the basis of price range.

* Is the difference in amount of bookings a noticeable feature for different neighbourhoods and reason for it.

* Hosts with the most number of bookings and the reason behind it.

* Looking into Room types and observing the bookings, availability, nights spent

* Checking the price based on availability of rooms

### 1. The relation between the price and neighbourhood group

![image](https://user-images.githubusercontent.com/48850318/212532112-82fd6b66-1e0c-404d-9312-845625ede39d.png)

### 2. The neighbourhood with most traffic (in terms of bookings).

![image](https://user-images.githubusercontent.com/48850318/212532143-18e867b9-f9dc-4dfd-96e8-13361a53c622.png)

### 3. Preference of customers on the basis of price range

![image](https://user-images.githubusercontent.com/48850318/212532187-3cc3d92b-0f50-4fe8-a0ed-359324a247ef.png)

### 4. Number of bookings vs room types

![image](https://user-images.githubusercontent.com/48850318/212532215-a91388ae-8299-4a2e-95d6-afb61e2ae2d7.png)

### 5. Hosts with the most number of bookings

![image](https://user-images.githubusercontent.com/48850318/212532255-be2ccf73-2445-4b81-8953-87150aa17744.png)

### 6. Lowest 10 listing count neighbourhood

![image](https://user-images.githubusercontent.com/48850318/212532295-8c7ebbab-1fa9-4d8d-86ec-0255271144dc.png)

### 7. Checking the price based on availability of rooms

![image](https://user-images.githubusercontent.com/48850318/212532333-ca114a61-c7e9-4317-9366-b0de53326a84.png)

### Correlation Heatmap

![image](https://user-images.githubusercontent.com/48850318/212532351-be823724-78bb-4220-ac40-d6a29fd2b8ee.png)