NYC, is renowned for its vibrancy and tourist appeal, hosts a myriad of airbnb listings, providing an intriguing dataset for exploring urban lodging dynamics.This project aim to dissect the 2019 Airbnb listing dataset for NYC, aiming to uncover underlying patterns and potentially predict pivotal factors such as pricing or availability, offering insights to hosts and platform strategis.
The dataset encompasses 48,895 Airbnb listings in NYC for year 2019, entailing 16 attributes that relay diverse aspect of listing. The dataset comprises both numerical and categorical data, with some missing value which was pre-processed.
- Can ML time series model unveil any prices and availability change over time(Seasons, Months, Special Events)?
- Is there a pattern in the minimum nights sets by hosts for their listings?
- Are there evident patterns in reviews or booking across different time of the year?
- Clone this repo or download all .py files and final.ipynd
- Acquire dataset from kaggle:
- Note: You need to be logged into your kaggle account in order to download csv files.
- Click the 'download' buttom at the top right of the page it will allow you to download.
- Once downloaded move csv to the directory/folder you are going to work on.
- https://www.kaggle.com/datasets/dgomonov/new-york-city-airbnb-open-data/data
- Afetr acquiring the data prepare the data.
- Explore data utilizing and stats test.
- Prepare data for modeling.
Statistical Hypothesis Testing
Planning Process
Exploratory Analysis
Key Findings
Modeling
Product Delivery