Airbnb Data Analysis: Enhancing Host Strategies

Project Overview

This project analyzes Airbnb hosting strategies using data analytics to optimize pricing, maximize occupancy rates, and improve guest experiences.

Key Focus Areas:

• Understanding how amenities impact pricing and guest ratings
• Identifying seasonal trends and dynamic pricing strategies
• Analyzing neighborhood-wise demand and investment potential
• Examining host behavior and its effect on occupancy rates

The insights from this analysis can help Airbnb hosts and investors make data-driven decisions to improve profitability.

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Dataset Description

This analysis is based on publicly available datasets from Inside Airbnb (Get the Data), focusing on San Diego, California.

Datasets Used:

Listings Dataset – Includes property details, pricing, amenities, and host information.
Calendar Dataset – Provides daily availability, price fluctuations, and seasonal demand trends.
Reviews Dataset – Captures guest feedback, review scores, and sentiment analysis.

By merging these datasets, guest preferences, pricing trends, and booking behavior were analyzed.

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Data Preprocessing

Before conducting the analysis, data preprocessing was performed with the following steps:

✔️ Removed unnecessary columns (URLs, metadata, redundant fields).
✔️ Handled missing values (using averages, replacing nulls with 'Unknown').
✔️ Standardized text & date formats (converted symbols, handled time-based calculations).
✔️ Created new features, including:

• Duration of host activity
• Number of reviews per month
• Adjusted bathroom counts based on privacy (private/shared)

After preprocessing, the datasets were merged into a unified dataset for in-depth analysis.

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📈 Key Findings

1️⃣ Guest Experience Insights

• Cleanliness, location, and spaciousness are the most critical factors influencing guest ratings.
• Listings with kitchens and free Wi-Fi tend to receive higher ratings.

2️⃣ Pricing & Seasonal Trends

• Prices peak in December due to holiday demand.
• Mid-week and weekend rates are consistently higher.

3️⃣ Neighborhood Analysis

• Downtown San Diego has the highest number of bookings.
• Local hosts receive better reviews than corporate-run listings.

4️⃣ Host Performance

• Superhosts have higher occupancy rates and better ratings than regular hosts.
• Faster response time and higher acceptance rates lead to improved revenue.

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Tools & Technologies Used

🔹 Python – Data processing and analysis (pandas, numpy)
🔹 Data Visualization – matplotlib.pyplot, seaborn, folium (interactive maps)
🔹 Statistical Analysis – scipy (T-tests, correlations)
🔹 Text Processing – wordcloud, CountVectorizer