Project Title : Airbnb Analysis.
Linkedin URL : https://www.linkedin.com/in/saravanan-b-241468269/details/projects/
Skills take away From This Project : Python scripting, Data Preprocessing, Visualization, EDA, Streamlit, MongoDb.
Domain : Travel Industry, Property Management and Tourism.
Problem Statement:
1. Clean and prepare the dataset, addressing missing values, duplicates, and data type conversions for accurate analysis.
2. Develop a streamlit web application with interactive maps showcasing the distribution of Airbnb listings, allowing users to explore prices, ratings, and other relevant factors.
3. Conduct price analysis and visualization, exploring variations based on location, property type, and seasons using dynamic plots and charts.
4. Analyze availability patterns across seasons, visualizing occupancy rates and demand fluctuations using suitable visualizations.
5. Investigate location-based insights by extracting and visualizing data for specific regions or neighborhoods.
6. Create interactive visualizations that enable users to filter and drill down into the data.
7. Build a comprehensive dashboard using Tableau or Power BI, combining various visualizations to present key insights from the analysis.
Approach:
1. Data Cleaning and Preparation: Clean the Airbnb dataset by handling missing values, removing duplicates, and transforming data types as necessary. Prepare the dataset for EDA and visualization tasks, ensuring data integrity and consistency.
2. Geospatial Visualization: Develop a streamlit web application that utilizes the geospatial data from the Airbnb dataset to create interactive maps. Visualize the distribution of listings across different locations, allowing users to explore prices, ratings, and other relevant factors.
3. Price Analysis and Visualization: Use the cleaned data to analyze and visualize how prices vary across different locations, property types, and seasons. Create dynamic plots and charts that enable users to explore price trends, outliers, and correlations with other variables.
4. Availability Analysis by Season: Analyze the availability of Airbnb listings based on seasonal variations. Visualize the occupancy rates, booking patterns, and demand fluctuations throughout the year using line charts, heatmaps, or other suitable visualizations.
5. Location-Based Insights: Investigate how the price of listings varies across different locations. Use MongoDB queries and data aggregation techniques to extract relevant information for specific regions or neighborhoods. Visualize these insights on interactive maps or create dashboards in tools like Tableau or Power BI.
6. Interactive Visualizations: Develop dynamic and interactive visualizations that allow users to filter and drill down into the data based on their preferences. Enable users to interact with the visualizations to explore specific regions, property types, or time periods of interest.
7. Dashboard Creation: Utilize Tableau or Power BI or Streamlit to create a comprehensive dashboard that presents key insights from your analysis. Combine different visualizations, such as maps, charts, and tables, to provide a holistic view of the Airbnb dataset and its patterns.
The learning outcomes of this project are:
1. Streamlit Web Application: Build a user-friendly web application using Streamlit, enhancing skills in web application development for interactive data exploration and visualization.
2. Python Data Analysis: Utilize Python for data cleaning, analysis, and visualization tasks, developing expertise in Python libraries such as Pandas and NumPy for data manipulation.
3. Geospatial Analysis: Leverage geospatial libraries like GeoPandas or Folium for geospatial data processing and visualization, gaining knowledge and skills in analyzing and visualizing spatial patterns.
4. Data Cleaning and Preparation: Develop proficiency in cleaning and preparing the Airbnb dataset, including handling missing values, duplicates, and data type conversions, ensuring data quality and consistency.
5. Data Visualization Techniques: Master data visualization techniques to effectively communicate insights, developing skills in creating visually appealing and informative charts, maps, and plots.
6. Problem-Solving Skills: Apply analytical skills to analyze pricing dynamics, availability patterns, and other factors, developing problem-solving abilities in extracting valuable insights from data.
7. Data-Driven Decision Making: Enhance decision-making skills by enabling stakeholders to make informed choices based on the insights and visualizations provided by the project.
8. Collaboration and Project Management: Strengthen collaboration and project management skills through the end-to-end development of the project, including task planning, coordination, and timely delivery of project milestones.