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The objectives are to:\n\n## 1. Establish MongoDB Connection:\n\nConnect to MongoDB Atlas to retrieve the Airbnb dataset.\nEnsure efficient data retrieval for analysis.\n\n## 2. Data Cleaning and Preparation:\n\nAddress missing values, duplicates, and perform data type conversions for accurate analysis.\n\n## 3.Develop Streamlit Web Application:\n\nCreate a user-friendly web application using Streamlit.\nInclude interactive maps showcasing the distribution of Airbnb listings.\nAllow users to explore prices, ratings, and other relevant factors.\n\n## 4.Visualization:\n using dynamic plots and charts.Explore variations based on location, property type, and seasons.\n\n\n## 6.Interactive Visualizations:\n\nCreate interactive visualizations that enable users to filter and drill down into the data.\n\n\n\n## Required Python Packages\n\nTo install the packages in python\n```\npip install streamlit pymongo pandas plotly\n\n```\n\n# MongoDB Setup:\n\n1.Create a [MongoDB Atlas](https://www.mongodb.com/cloud/atlas/efficiency?utm_content=rlsavisitor\u0026utm_source=google\u0026utm_campaign=search_gs_pl_evergreen_atlas_core_retarget-brand_gic-null_apac-all_ps-all_desktop_eng_lead\u0026utm_term=mongodb%20atlas\u0026utm_medium=cpc_paid_search\u0026utm_ad=e\u0026utm_ad_campaign_id=14412646476\u0026adgroup=131761130772\u0026cq_cmp=14412646476\u0026gad=1\u0026gclid=EAIaIQobChMIp8zhuOSEgAMVTw2DAx0aewI4EAAYASABEgIv__D_BwE)Account\n\n2.Set Up a Cluster\n\n3.Load the Airbnb Sample Data\n\n4.Import Sample Data\n\nSet up a MongoDB Atlas account and obtain connection details.\n\n\n# Streamlit Web Application:\n\n\nExplore the dynamic plots and charts generated by running \n```\nstreamlit run ./home.py\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmoha-cm%2Fairbnb-data-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmoha-cm%2Fairbnb-data-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmoha-cm%2Fairbnb-data-analysis/lists"}