Led a comprehensive data analysis project using Power BI to provide actionable insights for airline and airport authorities. Dataset: https://www.kaggle.com/datasets/dgomonov/new-york-city-airbnb-open-data
Given dataset include all information about host, listed properties, geographical location, prices reviews and all other required metrics. Analyse the given dataset make different predictions and draw meaningful conclusion in order to grow the business. Also state what can we learn from different predictions.
- What can we learn about different hosts and areas?
- What can we learn from predictions? (ex: locations, prices, reviews, etc)
- Which hosts are the busiest and why?
- Is there any noticeable difference of traffic among different areas and what could be the reason for it?
- In which Neighbourhood group there is maximum number of properties listed ?
- Which host has maximum number of properties listed ?
- Which host has maximum properties listed in neighbourhood groups having maximum properties listed ?
- What is the average price in different properties listed ?
- What may be the reason of having high price in that neighbourhood groups ?
- What is the most prefered room type in the every neighbourhood groups ?
- Total availability of properties having different room type?
- Which one is the busiest host ?
- Which property has maximum number of reviews ?
- Count of reviews per month
- Show total room types
- Find the total number of shared rooms, private rooms, entire home/apt
- Create a slicer for dates to show last reviewed information
- The prices for each neighborhood group
- Create a table for host to check the count of properties been listed for each neighborhood group.
- Improved operational efficiency and passenger experience.
- Enhanced financial performance tracking and resource management.
- Provided clear and transparent reporting for stakeholders, supporting strategic decisions.