Since 2008, guests and hosts have used AirBNB to expand on travelling possibilities and present more unique, personalized way of experiencing the world. This dataset describes the listing activity and metrics in Amsterdam for 2017. The analysis aims to draw insights from the data obtained from the listing activity of homestays in Amsterdam. Some research questions need to be answered with respect to all the listings of past booking information. To draw insights develop a report by Extracting-Transforming-Loading of data which contains listings of the past booking information. Analyze the data with respect to the Host, the neighbourhood, and customer pricing and reviews.
- Excel
- Jupyter notebook
Watch the complete Analysis video Link
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Top Earners: The highest earners are Host IDs 2674028, 336950, and 1464510. These hosts likely have multiple listings, high-priced listings, or both, potentially offering premium services or being located in high-demand areas.
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Monthly Earnings vs. Prices: There is a weak positive correlation (0.153) between monthly earnings and prices, meaning higher prices tend to lead to higher earnings, but the relationship is not very strong. Despite this, the correlation is statistically significant (p-value of 2.197e-84), indicating a genuine relationship.
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Bookings: The "De Baarsjes / Oud West" neighborhood leads in bookings with 16.9% of the total bookings, closely followed by "Centrum West" with 16.6%. These areas are likely popular due to their amenities, attractions, or high number of listings.
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Price Differences by Neighborhood: There is a statistically significant difference in average prices between "De Baarsjes / Oud West" and "De Pijp / Rivierenbuurt," with prices in "De Baarsjes / Oud West" being lower (t-statistic: -4.208, p-value: 2.62e-05).
- Quality vs. Price: There is a very weak negative correlation (-0.045) between overall satisfaction scores and price, suggesting that higher prices are associated with slightly lower satisfaction scores. This relationship is statistically significant (p-value: 7.32e-10).
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Room Types:
- Shared Room vs. Entire Home/Apt: Shared rooms are significantly cheaper than entire homes/apartments (t-statistic: -5.3936, p-value: 7.0101e-08).
- Shared Room vs. Private Room: No significant price difference between shared rooms and private rooms (t-statistic: -0.5092, p-value: 0.6106).
- Entire Home/Apt vs. Private Room: Entire homes/apartments are significantly more expensive than private rooms (t-statistic: 38.6702, p-value: 0.0).
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Price vs. Accommodates: There is a moderate positive correlation (0.5) between price and the number of people a property can accommodate, indicating that as accommodation capacity increases, so does the price. Differences in pricing based on accommodation capacity are generally statistically significant.
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Price vs. Bedrooms: A moderate positive correlation (0.45) exists between price and the number of bedrooms, meaning that more bedrooms typically lead to higher prices. Most comparisons of prices based on bedroom count are statistically significant.
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Price vs. Location: Price variations by location have been previously covered in the neighborhood analysis.
This summary provides an overview of key insights related to host earnings, neighborhood popularity, review ratings, and pricing trends based on various factors.
This findings can help hosts optimize their listings and pricing strategies based on location, amenities, and guest preferences. They provide valuable insights for potential hosts on where to list their properties and how to enhance guest satisfaction.
High Level Document (HLD)
Low Level Design Document (LLD)
Architecture
WireFrame
Detailed Project_Report
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