mostafa-fathy-0 / aibnb-eda Goto Github PK

The provided code is designed to analyze a dataset of listings in the short-term rental industry. It offers insights into the availability, pricing, and distribution of rooms across different room types and neighborhoods, along with recommendations for decision-makers in the industry.

The code begins by loading the dataset, assumed to be in a CSV format, containing information about the listings. It then defines several functions to answer specific questions and generate visualizations based on the data.

The first function, rooms_availability, groups the listings by room type and calculates the number of available rooms for each type. It generates a bar chart that visualizes the number of available rooms per room type, providing an overview of the inventory distribution.

The second function, average_price, calculates the average price per night for each room type. It creates a bar chart to compare the average prices across different room types, allowing decision-makers to identify pricing trends and make informed pricing decisions.

The third function, highest_price, determines the highest price per night for each room type. It generates a bar chart highlighting the maximum prices, offering insights into the upper range of pricing within each room type.

The fourth function, lowest_price, finds the lowest price per night for each room type. It produces a bar chart displaying the minimum prices, giving decision-makers an understanding of the lower range of pricing within each room type.

The fifth function, neighborhood_count, counts the number of listings in each neighborhood. It visualizes the distribution of listings across neighborhoods using a bar chart, providing an overview of popular areas and potential opportunities for expansion or targeted marketing.

Towards the end of the code, there is an aggregation of the insights derived from the data analysis. The insights highlight the variations in room availability, pricing, and distribution, empowering decision-makers to make data-driven choices.

The code concludes with recommendations for decision-makers based on the insights gained from the analysis. These recommendations emphasize the importance of assessing demand, monitoring competitors' pricing strategies, identifying potential growth areas, staying informed about market trends, and prioritizing customer satisfaction.

Overall, this code serves as a valuable tool for decision-makers in the short-term rental industry, enabling them to extract insights from data, gain a deeper understanding of the market, and make strategic decisions to optimize their offerings and maximize profitability.