Create README.md
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README.md
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# EDA Summary — Airbnb Open Data
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## 1. Dataset Overview
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The dataset consists of 102,599 Airbnb listings in the United States. It includes 26 original features covering listing attributes, host characteristics, availability details, and review metrics.
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### Key Feature Types (full list in the notebook)
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**Categorical Features:**
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- `room_type`
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- `neighbourhood_group`
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- `host_identity_verified`
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- `cancellation_policy`
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- `instant_bookable`
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**Numerical Features:**
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- `price`
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- `service_fee`
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- `review_rate_number`
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- `minimum_nights`
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- `number_of_reviews`
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- `availability_365`
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### Prediction Goal
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The primary task is a regression problem:
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Predict the numeric `review_rate_number` (1.0–5.0) for each listing based on features such as price, room type, and neighbourhood group.
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---
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## 2. Data Cleaning
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A series of preprocessing steps were performed to prepare the dataset for analysis and modeling.
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### Columns Removed
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- `host_id`, `host_name`, `name` — Identifier/text fields not useful for prediction
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- `country_code`, `country` — All entries are within the US
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- `cancellation_policy`, `house_rules` — Non-numeric and removable per instructions
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- `last_review` — Not relevant to the analysis
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- `license` — Mostly empty
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### Data Quality Fixes
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- Corrected spelling issues in `neighbourhood_group`:
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- "brooklm" → Brooklyn
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- "manhatan" → Manhattan
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- Handled rare categories by checking frequency
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- Filled missing values
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- Resolved contradictions (e.g., listings with 0 reviews but a rating)
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- Removed duplicate listings using the `id` field
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### Outliers handling
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I handled outliers using box plot:
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it is noticable that minumus nights has outliers as it is not possible to have negative value, and it is weird to have values larger than 365
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**number of reviews and reviews per month** : I see a lot of data points far from the regular range.
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That popular listing is an outlier, but it's one of the most important data points! We definitely keep these, it just implies about the property popularity.
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**calculated host listings count** - there are many "Far" data point but, it might be "superhosts" or property managers who own many listings. we will keep it.
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---
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## 3. Visualizations and Insights
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The EDA included various visual explorations of the dataset to examine relationships between listing characteristics and review ratings.
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The prices per room type varies, and there doens't seem to be an outlier. no changes will be done to the data.
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---
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## 4. Research Questions and Findings
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### Q1 — Is Room Type Related to Being Highly Rated?
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Room type shows only a weak relationship with high ratings.
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- Hotel rooms have the highest proportion of 5-star reviews
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- Shared rooms have the lowest
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- Overall, differences across room types are modest, suggesting room type alone is not a strong predictor of guest satisfaction
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### Q2 — What Is the Price Distribution of Highly-Rated vs. Not-Highly-Rated Listings?
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Key observations:
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- Listings with a rating of 0 tend to have much lower median prices
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- This is likely because new or unrated properties set lower prices to attract initial guests
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- Once listings begin receiving ratings, their prices increase and become more consistent
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This suggests that hosts raise prices after building credibility through reviews.
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---
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### Q3 — Does Neighbourhood Group Affect Rating?
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There is an important difference between raw counts and percentages:
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- Manhattan has the highest number of 5-star reviews because it has the most listings
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- Staten Island and Queens have the highest proportion of 5-star ratings
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This indicates that while Manhattan has high volume, its rating consistency is lower. In contrast, smaller markets like Staten Island show more uniform guest satisfaction.
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---
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### Q4 — Does the Number of Listings a Host Manages Influence Their Ratings?
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A clear pattern emerges:
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- Hosts who manage more properties tend to have higher average ratings
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This may suggest that professional or experienced hosts provide more consistent service and maintain higher-quality listings.
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