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Add Airbnb Data Analysis Project under datascience folder #1799 #1801

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Binary file added Data_Science/Airbnb-Data-Analysis/AIRBNB PROJECT.pptx
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34 changes: 34 additions & 0 deletions Data_Science/Airbnb-Data-Analysis/README.md
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# Airbnb Data Analysis Project

## Overview
This project provides a comprehensive analysis of Airbnb listing data to uncover trends in pricing, availability, and other key factors. Using Tableau for visualization, the project explores the Airbnb rental market, highlighting patterns based on geographic location, room types, and pricing variations.
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⚠️ Potential issue

Add data source, license, and non‑affiliation/privacy notes.

Provenance and licensing are required for compliance and reuse; add a short disclaimer on Airbnb affiliation and PII handling.

Apply:

 ## Overview
 This project provides a comprehensive analysis of Airbnb listing data to uncover trends in pricing, availability, and other key factors. Using Tableau for visualization, the project explores the Airbnb rental market, highlighting patterns based on geographic location, room types, and pricing variations.
+
+## Data Source & Licensing
+- Source: <dataset name/provider, URL, retrieval date>.
+- License: <dataset license>; confirm that redistribution of the workbook and excerpts complies.
+- Privacy: dataset must not include precise addresses, emails, phone numbers, or other PII. Any such fields should be removed or generalized.
+- Non‑affiliation: This project is for educational purposes and is not affiliated with or endorsed by Airbnb.
📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
## Overview
This project provides a comprehensive analysis of Airbnb listing data to uncover trends in pricing, availability, and other key factors. Using Tableau for visualization, the project explores the Airbnb rental market, highlighting patterns based on geographic location, room types, and pricing variations.
## Overview
This project provides a comprehensive analysis of Airbnb listing data to uncover trends in pricing, availability, and other key factors. Using Tableau for visualization, the project explores the Airbnb rental market, highlighting patterns based on geographic location, room types, and pricing variations.
## Data Source & Licensing
- Source: <dataset name/provider, URL, retrieval date>.
- License: <dataset license>; confirm that redistribution of the workbook and excerpts complies.
- Privacy: dataset must not include precise addresses, emails, phone numbers, or other PII. Any such fields should be removed or generalized.
- Non-affiliation: This project is for educational purposes and is not affiliated with or endorsed by Airbnb.
🤖 Prompt for AI Agents 
In Data_Science/Airbnb-Data-Analysis/README.md around lines 3-4, add brief
provenance, licensing and privacy/non‑affiliation statements: specify the
original data source (e.g., "Dataset obtained from Airbnb public listings via
[source/link] on [date]" or the exact file name), include a license or reuse
terms (e.g., CC BY‑NC or link to dataset's license) and a short non‑affiliation
disclaimer ("This project is not affiliated with Airbnb"), and add a PII/privacy
note explaining any personal data removal/anonymization steps and that no
attempt was made to contact hosts/guests. Keep each note one or two sentences
and place them under a new "Data Sources and License" or "Provenance and
Privacy" section near the top of the README.


## Project Files
- **AirbnbProject.pptx**: PowerPoint presentation summarizing the main insights from the analysis.
- **TableauProject.twbx**: Tableau workbook for creating and exploring interactive visualizations.

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⚠️ Potential issue

🧩 Analysis chain

Include the Excel dataset and reconcile file names; avoid spaces/case drift.

  • Project files list omits the Excel dataset mentioned in the PR objectives.
  • Filenames differ from the PR text (e.g., “AIRBNB PROJECT.pptx” vs “AirbnbProject.pptx”). Normalize and match the actual committed files to prevent broken references across OSes.

Apply this minimal update (adjust names to whatever is actually in the repo):

 ## Project Files
-- **AirbnbProject.pptx**: PowerPoint presentation summarizing the main insights from the analysis.
-- **TableauProject.twbx**: Tableau workbook for creating and exploring interactive visualizations.
+- **Airbnb-Project.pptx**: PowerPoint presentation summarizing the main insights from the analysis.
+- **TableauProject.twbx**: Tableau workbook for creating and exploring interactive visualizations.
+- **data/Airbnb_Listings.xlsx**: Source dataset used by the workbook (if not embedded in the TWBX).

Run this to verify filenames and the folder that actually exists:

🏁 Script executed:

#!/usr/bin/env bash
set -euo pipefail
for d in Data_Science/Airbnb-Data-Analysis datascience/Airbnb-Data-Analysis; do
  if [ -d "$d" ]; then
    echo "Contents of $d:"
    ls -al "$d"
  fi
done

Length of output: 377

Normalize filenames in README and add the missing Excel dataset

  • Repo (Data_Science/Airbnb-Data-Analysis) contains: AIRBNB PROJECT.pptx and TableauProject.twbx; README lists AirbnbProject.pptx and mentions an Excel dataset that is not present.
  • Action (choose one):
    • Rename Data_Science/Airbnb-Data-Analysis/"AIRBNB PROJECT.pptx" → Data_Science/Airbnb-Data-Analysis/AirbnbProject.pptx (remove spaces/case drift) and add the source dataset at data/Airbnb_Listings.xlsx if it's not embedded in the TWBX.
    • OR update README to reference the exact committed filenames (Data_Science/Airbnb-Data-Analysis/"AIRBNB PROJECT.pptx", TableauProject.twbx) and explicitly state whether the source dataset is embedded in the TWBX or where the Excel file is located.
  • Use consistent, space-free filenames for cross-OS compatibility.
🤖 Prompt for AI Agents 
In Data_Science/Airbnb-Data-Analysis/README.md around lines 6–9, the README
mentions "AirbnbProject.pptx" and an Excel dataset that do not match the repo
contents (the repo contains "AIRBNB PROJECT.pptx" and TableauProject.twbx and
the Excel file is missing or embedded); fix by either (A) renaming the repo file
to AirbnbProject.pptx (remove spaces/change case) and adding the source dataset
at Data_Science/Airbnb-Data-Analysis/data/Airbnb_Listings.xlsx if it is not
embedded in the TWBX, or (B) update the README to reference the exact committed
filenames ("AIRBNB PROJECT.pptx" and "TableauProject.twbx") and explicitly state
whether the source dataset is embedded in the TWBX or where the Excel file can
be found; ensure filenames use consistent, space-free naming if you choose
rename, and commit the README and any file renames/additions together.

⚠️ Potential issue

🧩 Analysis chain

Track large binaries with Git LFS to keep repo lean.

TWBX/PPTX (and XLSX) often exceed normal size thresholds; ensure they’re LFS‑tracked.

Use this check:

🏁 Script executed:

#!/usr/bin/env bash
set -euo pipefail
echo "Potential large binaries:"
python - <<'PY'
import os
exts={'.twbx','.twb','.pptx','.xlsx','.xls','.csv'}
for root,_,files in os.walk('.'):
  for f in files:
    if os.path.splitext(f)[1].lower() in exts:
      p=os.path.join(root,f)
      try:
        sz=os.path.getsize(p)
      except OSError:
        continue
      if sz>=5*1024*1024:
        print(f"{p}\t{sz/1024/1024:.1f} MB")
PY
echo
echo ".gitattributes entries referencing LFS:"
[ -f .gitattributes ] && rg -n 'filter=lfs' .gitattributes || echo "No .gitattributes found"

Length of output: 1760

Track large binaries with Git LFS and migrate existing large files.

No .gitattributes found; the repo contains multiple files >=5MB. Add a .gitattributes and LFS-track binary/artifact patterns (e.g. *.twbx, *.pptx, *.xlsx, *.xls and large *.csv), then migrate the existing large files into LFS or move them to external storage (git lfs migrate import --include="path,...").
Notable large files found:

  • Data_Science/Airbnb-Data-Analysis/TableauProject.twbx — 5.1 MB
  • Machine_Learning/Book Recommendation System/books.csv — 73.9 MB
  • Deep_Learning/toxicity insult analyser/jigsaw-toxic-comment-classification-challenge/train.csv/train.csv — 65.6 MB
  • Deep_Learning/toxicity insult analyser/jigsaw-toxic-comment-classification-challenge/test.csv/test.csv — 57.6 MB
  • Machine_Learning/Fake news detection project/dataset/Fake.csv — 59.9 MB
  • Machine_Learning/Fake news detection project/dataset/True.csv — 51.1 MB

## Objectives
The main objectives of this analysis are to:
- Analyze Average Price per Bedroom: Assess price variations based on the number of bedrooms.
- Visualize Price Distribution by Zip Code: Identify geographic patterns in pricing.
- Evaluate Weekly Revenue Trends: Observe seasonal and location-based revenue trends.
- Analyze Listings by Bed Type and Bedroom Count: Gain insights into the types of listings and bed availability.

## Key Insights
- **Average Price Per Bedroom**: Larger properties generally have higher average prices, with steep increases for properties with 4+ bedrooms.
- **Price by Zip Code**: Some zip codes show significantly higher prices, possibly indicating more desirable or premium areas.
- **Revenue Trends**: Weekly revenue peaks during certain seasons, reflecting high-travel demand periods.
- **Common Listing Types**: Real beds are the most frequent, and properties with 1-2 bedrooms make up the majority of listings.

## Setup and Usage
To interact with the project and explore the data:
- Open **TableauProject.twbx**: Use Tableau Desktop to open the file and explore the visualizations.
- Review the Presentation: Open **AirbnbProject.pptx** to see summarized findings.

Comment on lines +23 to +27
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⚠️ Potential issue

Specify Tableau Desktop version and whether data is embedded; add relinking steps.

Without this, opening the TWBX may fail or silently point to the wrong data.

Apply:

 ## Setup and Usage
-To interact with the project and explore the data:
-- Open **TableauProject.twbx**: Use Tableau Desktop to open the file and explore the visualizations.
-- Review the Presentation: Open **AirbnbProject.pptx** to see summarized findings.
+To interact with the project and explore the data:
+- Prerequisite: Tableau Desktop (specify tested version, e.g., 2023.3 or later).
+- Open **TableauProject.twbx** in Tableau Desktop.
+  - If the workbook expects an external Excel file (not embedded), go to Data > [Data Source] > Edit Connection and relink to `data/Airbnb_Listings.xlsx`.
+  - If an extract is used, Data > [Data Source] > Extract > Refresh after relinking.
+- Review the presentation: open **Airbnb-Project.pptx** for summarized findings.
📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
## Setup and Usage
To interact with the project and explore the data:
- Open **TableauProject.twbx**: Use Tableau Desktop to open the file and explore the visualizations.
- Review the Presentation: Open **AirbnbProject.pptx** to see summarized findings.
## Setup and Usage
To interact with the project and explore the data:
- Prerequisite: Tableau Desktop (specify tested version, e.g., 2023.3 or later).
- Open **TableauProject.twbx** in Tableau Desktop.
- If the workbook expects an external Excel file (not embedded), go to Data > [Data Source] > Edit Connection and relink to `data/Airbnb_Listings.xlsx`.
- If an extract is used, Data > [Data Source] > Extract > Refresh after relinking.
- Review the presentation: open **Airbnb-Project.pptx** for summarized findings.
🤖 Prompt for AI Agents 
In Data_Science/Airbnb-Data-Analysis/README.md around lines 23 to 27, the TWBX
instructions lack Tableau version and data-link guidance; update the README to
state the minimum Tableau Desktop version used (e.g., Tableau Desktop 2023.1)
and explicitly note whether the TWBX contains embedded/extracted data or
references external files, then add concise relinking steps: open Tableau
Desktop, go to Data > Replace Data Source (or Data > Extract > Refresh) if the
workbook points to external CSV/Hyper files, and provide the expected relative
path(s) to the data files in the repo (or note that data is embedded so no
relinking is needed).

## Future Enhancements
Potential future improvements include:
- Adding More Data Features: Incorporating additional features like reviews or amenities.
- Predictive Analysis: Using machine learning to predict pricing trends based on data.
- Interactive Web Dashboard: Creating a web-based, interactive dashboard for public access.


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