https://github.com/mafda/seattle_airbnb_data_analysis
This repository contains a comprehensive analysis of the Seattle Airbnb dataset, conducted using the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology.
https://github.com/mafda/seattle_airbnb_data_analysis
crisp-dm data-analysis data-science jupyter-notebook pandas-python seattle-data
Last synced: 8 months ago
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This repository contains a comprehensive analysis of the Seattle Airbnb dataset, conducted using the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology.
- Host: GitHub
- URL: https://github.com/mafda/seattle_airbnb_data_analysis
- Owner: mafda
- Created: 2024-06-18T11:34:16.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-03T05:57:45.000Z (over 1 year ago)
- Last Synced: 2025-01-17T05:09:16.045Z (10 months ago)
- Topics: crisp-dm, data-analysis, data-science, jupyter-notebook, pandas-python, seattle-data
- Language: Jupyter Notebook
- Homepage: https://medium.com/@mafda/seattle-airbnb-data-analysis-a-data-driven-journey-with-crisp-dm-for-data-scientists-b66b5672c617
- Size: 1.95 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Seattle Airbnb Data Analysis
The Seattle Airbnb dataset is a comprehensive collection of data related to
Airbnb listings in Seattle. It provides detailed information across several
aspects of the Airbnb ecosystem, including listings, calendar availability, and
user reviews.
The Seattle Airbnb dataset offers a rich source of information for analyzing
various aspects of Airbnb listings in Seattle, including trends in pricing and
availability, geographic distribution of listings, and customer satisfaction
based on reviews. This dataset is valuable for conducting detailed exploratory
data analysis (EDA), machine learning projects, and deriving insights into the
short-term rental market in Seattle.
The dataset is divided into three main files:
- `listings.csv`: Contains detailed information about each Airbnb listing in
Seattle.
- `calendar.csv`: Provides daily availability and pricing information for each
listing.
- `reviews.csv`: Contains user reviews for each listing.
## Project Setup
### Clone this repository
```shell
(base)$: git clone git@github.com:mafda/seattle_airbnb_data_analysis.git
(base)$: cd seattle_airbnb_data_analysis
```
### Configure environment
- Create the conda environment
```shell
(base)$: conda env create -f environment.yml
```
- Activate the environment
```shell
(base)$: conda activate seattle_airbnb
```
- Download the dataset from [Seattle Airbnb Open
Data](https://www.kaggle.com/datasets/airbnb/seattle/data), create `data`
folder and copy the data here.
```shell
(seattle_airbnb)$: mkdir data
```
## Project Structure
```shell
.
├── README.md
├── data
│ ├── calendar.csv
│ ├── listings.csv
│ └── reviews.csv
├── environment.yml
└── src
└── seattle_airbnb_data_analysis.ipynb
```
## Metodology
This project will follow [CRoss Industry Standard Process for Data Mining -
CRISP-DM](https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_mining)
methodology.
### Key Highlights
- **Business Understanding**: Identify the key business questions, such as
factors influencing occupancy rates, pricing strategies, and customer
satisfaction.
- **Data Understanding**: Explore the structure and content of the dataset,
including listings.csv, calendar.csv, and reviews.csv.
- **Data Preparation**: Clean and pre-process the data by handling missing
values and converting data types.
- **Modeling**: Apply statistical and machine learning models to analyze the
linear relationship between features and price. Use visualization techniques
to illustrate the relationships.
- **Evaluation**: Validate the models and analysis results for accuracy and
reliability.
- **Deployment**: Document the insights and recommendations based on the
analysis. Provide actionable strategies for optimizing listings, pricing, and
improving customer experience.
## Findings
Some exploratory analysis.
### Identify temporal patterns in reserves and prices
The analysis reveals a relationship between occupancy rates and prices. This
indicates a seasonal trend with peaks in winter (January) and summer (July),
followed by gradual decreases. This correlation suggests that higher occupancy
rates, likely driven by seasonal demand, are associated with higher prices.
### Determine factors that influence property prices.
The correlation matrix shows that there is a high correlation between price and
review-related variables (such as number of reviews and review score).
Significant correlations are also observed with the number of bathrooms, beds,
bedrooms, accommodation capacity and number of guests included. This suggests
that these factors are key determinants of listing prices, with larger,
better-reviewed properties tending to command higher prices.
### Evaluate customer satisfaction based on reviews
The most frequently used words in user reviews were "great," "clean,"
"location," and "comfortable." These keywords suggest that customers generally
had positive experiences and appreciated the cleanliness, location, and comfort
of the accommodations. The analysis shows that customer satisfaction is
overwhelmingly positive, with a few areas for improvement in handling
multilingual reviews.
## Data Modeling
**Linear Regression** is a simple model that attempts to capture the linear
relationship between **features and price**.
- Pros: Easy to interpret and quick to train.
- Cons: May not capture complex relationships between variables.
### Results
Although the model has a reasonable level of predictive ability, an R² of **0.558**
also suggests that there is **44.2%** of the variability that is not being
captured by the model, indicating that some important features could be
missing or that the model is not complex enough to capture all relationships
present in the data.
| Cost | Result |
| ------------------------- | ------ |
| Mean Absolute Error (MAE) | 38.09 |
| R² Score | 0.558 |
## Analysis of Results and Conclusions
Although the model has a reasonable level of predictive ability, an R² of 0.558 also suggests that there is 44.2% of the variability that is not being captured by the model, indicating that some important features could be missing or that the model is not complex enough to capture all relationships present in the data.
For a detailed analysis of the results obtained, visit our [Medium
blog](https://medium.com/@mafda/seattle-airbnb-data-analysis-a-data-driven-journey-with-crisp-dm-for-data-scientists-b66b5672c617).
Here you will find a comprehensive explanation of the findings and their
interpretation in the context of the Seattle Airbnb data analysis.
> [Seattle Airbnb: Temporal Trends, Price Influences, and Customer Satisfaction Analysis.](https://medium.com/@mafda/seattle-airbnb-data-analysis-a-data-driven-journey-with-crisp-dm-for-data-scientists-b66b5672c617)
## References
- [Data Scientist Nanodegree
Program](https://www.udacity.com/course/data-scientist-nanodegree--nd025)
- [Seattle Airbnb Dataset](https://www.kaggle.com/datasets/airbnb/seattle/data)
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