Welcome to the Airbnb Bookings EDA repository! This project showcases my expertise in exploratory data analysis (EDA), particularly using data visualization tools like matplotlib, seaborn, and pyplot. Below you'll find detailed descriptions of the files included and an overview of the analysis conducted.
- Main_Notebook.ipynb: The main Jupyter notebook containing all the analysis, visualizations, and insights.
- AirBnb_Bookings_Data.csv: The dataset used for the analysis.
To explore and analyze the dataset effectively and answer the listed questions, we need to perform various data analysis and visualization techniques. Here's a step-by-step outline to tackle each question:
- What can we learn about different hosts and areas
- What can we learn from room type and their prices according to area?
- What can we learn from Data? (ex: locations, prices, reviews, etc)
- Which hosts are the busiest and why is the reason?
- Which Hosts are charging higher prices?
- Is there any traffic difference among different areas and what could be the reason for it?
- What is the correlation between different variables?
- What is the room count in overall NYC according to the listing of room types?
This section focuses on preparing the data for analysis. The steps included:
- Data Cleaning: Handling missing values, correcting data types, and filtering irrelevant data.
- Data Manipulation: Creating new features, normalizing data, and preparing it for analysis.
- Modeling: Although the primary focus is EDA, basic statistical models were used to understand correlations and trends within the dataset.
Effective data visualization is key to uncovering insights. Utilizing my skills in matplotlib, seaborn, and pyplot, various plots were created to visually explore the dataset, including:
- Histograms: To understand the distribution of key variables.
- Scatter Plots: To identify relationships between different features.
- Box Plots: To detect outliers and understand the spread of the data.
- Heatmaps: To visualize correlations between variables.
Based on the analysis, several key insights were identified, leading to actionable business solutions:
- Targeted Marketing Strategies
- Host support and Development
- Enhanced User Experience
- Investment and propoert Development
- Customer Service Enchancements
- Pricing Strategies
- Loyalty Programs
Through this project, I have demonstrated the following skills:
- Data Visualization: Creating clear, compelling, and interactive visualizations using matplotlib, seaborn, and pyplot.
- Data Analysis: Analyzing booking data to extract actionable insights and identify key trends.
- Business Intelligence: Leveraging EDA to enhance decision-making processes and drive business performance.
- Data Manipulation: Cleaning and preparing data to ensure accuracy and reliability in visualizations.
- User Experience: Designing visualizations with the end-user in mind, ensuring ease of use and accessibility.
To explore the analysis, you can open the Jupyter notebook using the following steps:
-
Jupyter Notebook:
- Install Jupyter Notebook.
- Open the
Main_Notebook.ipynbfile.
-
Dataset:
- The
AirBnb_Bookings_Data.csvfile is included for reference and reproducibility of the analysis.
- The
This repository exemplifies my ability to perform thorough exploratory data analysis, from data cleaning and manipulation to visualization and deriving actionable insights. Feel free to explore the notebook and reach out if you have any questions or feedback.
For any inquiries or further information, you can reach me at:
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Thank you for visiting my repository! Happy analyzing!
Special thanks to Airbnb and AlmaBetter for providing the dataset and to the data science community for continuous support and inspiration.