https://github.com/moha-cm/airbnb-data-analysis

Airbnb Data retrival from MongoDb and Analying the Data
https://github.com/moha-cm/airbnb-data-analysis

dashboard-application data-preprocessing data-visualization eda mongodb nosql-database plotly python python-script python-scripting streamlit-application

Last synced: about 1 month ago
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Airbnb Data retrival from MongoDb and Analying the Data

• Host: GitHub
• URL: https://github.com/moha-cm/airbnb-data-analysis
• Owner: Moha-cm
• Created: 2023-12-02T12:11:34.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-12-02T14:54:11.000Z (about 1 year ago)
• Last Synced: 2024-01-29T10:11:53.315Z (11 months ago)
• Topics: dashboard-application, data-preprocessing, data-visualization, eda, mongodb, nosql-database, plotly, python, python-script, python-scripting, streamlit-application
• Language: Python
• Homepage:
• Size: 17.6 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## **Airbnb Data Analysis Project**

**Overview**

This project aims to analyze Airbnb data using MongoDB Atlas, perform data cleaning and preparation, develop interactive geospatial visualizations, and create dynamic plots to gain insights into pricing variations, availability patterns, and location-based trends. The objectives are to:

## 1. Establish MongoDB Connection:

Connect to MongoDB Atlas to retrieve the Airbnb dataset.

Ensure efficient data retrieval for analysis.

## 2. Data Cleaning and Preparation:

Address missing values, duplicates, and perform data type conversions for accurate analysis.

## 3.Develop Streamlit Web Application:

Create a user-friendly web application using Streamlit.

Include interactive maps showcasing the distribution of Airbnb listings.

Allow users to explore prices, ratings, and other relevant factors.

## 4.Visualization:

 using dynamic plots and charts.Explore variations based on location, property type, and seasons.

## 6.Interactive Visualizations:

Create interactive visualizations that enable users to filter and drill down into the data.

## Required Python Packages

To install the packages in python

```

pip install streamlit pymongo pandas plotly

```

# MongoDB Setup:

1.Create a [MongoDB Atlas](https://www.mongodb.com/cloud/atlas/efficiency?utm_content=rlsavisitor&utm_source=google&utm_campaign=search_gs_pl_evergreen_atlas_core_retarget-brand_gic-null_apac-all_ps-all_desktop_eng_lead&utm_term=mongodb%20atlas&utm_medium=cpc_paid_search&utm_ad=e&utm_ad_campaign_id=14412646476&adgroup=131761130772&cq_cmp=14412646476&gad=1&gclid=EAIaIQobChMIp8zhuOSEgAMVTw2DAx0aewI4EAAYASABEgIv__D_BwE)Account

2.Set Up a Cluster

3.Load the Airbnb Sample Data

4.Import Sample Data

Set up a MongoDB Atlas account and obtain connection details.

# Streamlit Web Application:

Explore the dynamic plots and charts generated by running 

```

streamlit run ./home.py

```