/Tableau-Project

Final-Project-Tableau

Project/Goals

The goal was to study the chosen dataset (Option2 Airbnb dataset) and to find interesting patterns or trends in data to come up with interesting conclusions about the dataset.

Process

Connecting data to Tableau

- Connected Airbnb.xlsx to analyze the data of Airbnb listings in New York City

Analyzing Data

- Created different visualizations to learn more about the data

Creating Questions

- Created questions after recognizing patterns in data

Conclusions

- Answering Questions and coming up with conclusions using data visualizations

Results

Option 2 : AIRBNB dataset

1. Which neighborhood has the most expensive Airbnb’s?

- Manhattan is the most expensive neighborhood for Airbnb’s in New York City.

(Map was created to come up with the solution for this question where the dots represent each zip code and the size of dots represented the price and colour representing the neighborhood. The map was creted because it can be used to easily visualize the most expensive area.)

2. Does the average rating score depend on the type of property?

- Lighthouses and Castles has the highest rating score (100). The review score was higher for the fancier places while normal property types such as houses/ apartments has lower ratings.

(Bar chart was created to represent this. y-axis is representing the property type and the x-axis is representing the rating score. Bar chart was picked in this case because it is easier to rank from highest to lowest)

3. Does the hosts who started earlier has more records compared to newer members? Predict the number of records for hosts started from 2015-2021.

- The number of Records that a host has is higher for new members compared to previous years. According to the forecast results the record number should be even higher for the members started between 2015-2020 and lower for members started in 2021. (could be because they started very recently)

(Line chart was used to represent the relationship between member since versus the number of records since it's easier to forecast the future data with a line chart)

4. Which room type is the most popular category?

- Entire home/apartment is the type of Airbnb’s that had the greatest number of record while shared rooms has the lowest records, indicating that Entire home/ apartment is the most popular category.

(Pie chart was created to answer this question to show which category takes the most area and can be easily visualized)

5. Does the review score depend number of Airbnb’s a host has?

- There seems to be no relationship between the number of Airbnb’s a host has versus their reviews. However, the 3 highest number of Airbnb’s a host has were 32, 30, 28 and their reviews were in the same range which is 88, 92,94.

(Scatterplot was created to show the relationship between count of airbnbs a host has versus average review score rating)

Challenges

Coming up with meaningful visualizations was a challenge since there's only a few categories of data were present in dataset compared to the other datasets given.

Future Goals

My future goal is to analyze the data more and studying the dataset more in depth to find out the hidden interesting patterns.