1. EXECUTIVE SUMMARY

The dataset I choose is the New York City Airbnb Listings, which can be found on Kaggle. This dataset is why I selected it: it offers a thorough picture of Airbnb activity in New York City, one of the world's liveliest cities. The price of Airbnb listings may be predicted, the factors influencing the price can be understood, the geographical distribution of Airbnb listings can be examined, and many other problems can be solved with the help of this dataset. I selected this dataset because to its ability to provide an extensive overview of the Airbnb industry in New York City, one of the most visited cities in the world. A thorough examination of this information may yield important insights into the variables influencing the cost and demand for Airbnb listings, as well as the influence of geography and home type on user preferences.

1. INTRODUCTION

The dataset is derived from the official Airbnb website and is updated on a daily basis by Inside Airbnb, an independent effort that attempts to illustrate how Airbnb affects residential neighborhoods.

3. DATA SELECTION APPROACH

: DATASET INFO The dataset has more than 100 rows and 5 fields. The fields include:

1. id: The listing ID (Numeric)
2. name: The name of the listing (Categorical)
3. host_id: The host ID (Numeric)
4. host_name: The name of the host (Categorical)
5. neighbourhood_group: The group of the neighborhood (Categorical)
6. neighbourhood: The neighborhood (Categorical)
7. latitude: The latitude of the listing (Numeric)
8. longitude: The longitude of the listing (Numeric)
9. room_type: The type of room (Categorical)
10. price: The price of the listing (Numeric)
11. minimum_nights: The minimum number of nights (Numeric)
12. number_of_reviews: The number of reviews (Numeric)
13. last_review: The date of the last review (Date)
14. reviews_per_month: The number of reviews per month (Numeric)
15. calculated_host_listings_count: The number of listings per host (Numeric)
16. availability_365: The number of days when the listing is available for booking (Numeric)

4. METHODS USED

To print the first few rows in Python, you would normally import the dataset using the pandas module and then use the head() function.

import pandasas pd

# Load the dataset

df= pd.read_csv('AB_NYC_2019.csv')

# Print the first few rows

print(df.head())

5. ANALYSIS AND RESULTS

5.1 Data Analysis Objective(s)

This section should describe the rationale for the data analysis.

Example: The dataset chosen provides information on the number of COVID19 testing and the number of individuals that were infected. The data analysis seeks to find out the ratio (correlation) of tested individuals from those who get infected with the virus.

• Independent variable - Number of tests
• Dependent variable - Number of infected cases