Airbnb Bookings EDA

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.

Repository Contents

• Main_Notebook.ipynb: The main Jupyter notebook containing all the analysis, visualizations, and insights.
• AirBnb_Bookings_Data.csv: The dataset used for the analysis.

Project Overview

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:

1. What can we learn about different hosts and areas
2. What can we learn from room type and their prices according to area?
3. What can we learn from Data? (ex: locations, prices, reviews, etc)
4. Which hosts are the busiest and why is the reason?
5. Which Hosts are charging higher prices?
6. Is there any traffic difference among different areas and what could be the reason for it?
7. What is the correlation between different variables?
8. What is the room count in overall NYC according to the listing of room types?

Data Preparation and Modeling

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.

Data Visualization

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.

Insights and Business Solutions