This Python-based project explores Airbnb pricing trends across New York City boroughs using real-world-inspired data to identify how location, room type, and reviews impact nightly rates.
To analyze Airbnb listings in NYC and answer key questions:
- Which boroughs have the highest and lowest average Airbnb prices?
- How does room type affect price?
- Is there a relationship between number of reviews, availability, and price?
- Python 3.11+
- Jupyter Notebook (or Google Colab)
- Libraries: Pandas, Matplotlib, Seaborn
- Data cleaning and formatting with
pandas - Grouping and aggregation
- Visual analysis with
matplotlibandseaborn - Correlation insights using scatter plots
- Exporting and saving visualizations
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📊 Average Airbnb Price by Borough
📈 Price vs. Number of Reviews
Use a full NYC dataset from Inside Airbnb or Kaggle
Incorporate geospatial visuals using folium or plotly
Add boxplots and statistical summaries
Explore simple regression or classification models to predict price or churn
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Sample Airbnb NYC dataset
data = {
'neighbourhood_group': ['Manhattan', 'Brooklyn', 'Queens', 'Manhattan', 'Brooklyn'],
'neighbourhood': ['Harlem', 'Williamsburg', 'Astoria', 'Midtown', 'Bushwick'],
'room_type': ['Entire home/apt', 'Private room', 'Entire home/apt', 'Private room', 'Entire home/apt'],
'price': [150, 80, 90, 100, 120],
'number_of_reviews': [100, 230, 45, 80, 190],
'availability_365': [200, 300, 150, 180, 220]
}
df = pd.DataFrame(data)
# Average price by borough
avg_price = df.groupby('neighbourhood_group')['price'].mean().reset_index()
sns.barplot(x='neighbourhood_group', y='price', data=avg_price)
plt.title('Average Airbnb Price by Borough')
plt.ylabel('Average Price ($)')
plt.xlabel('Borough')
plt.show()
# Price vs number of reviews
sns.scatterplot(x='number_of_reviews', y='price', hue='room_type', data=df)
plt.title('Price vs Number of Reviews')
plt.xlabel('Number of Reviews')
plt.ylabel('Price ($)')
plt.show()