Airbnb Booking Analysis & Optimization Dashboard
Overview
This project aims to analyze and optimize Airbnb booking data using advanced data analytics, machine learning models, and interactive visualizations. The objective was to uncover insights related to business performance, identify factors influencing perfect ratings, and develop a dashboard for real-time decision-making and business intelligence.
The solution involves cleaning and processing large datasets, calculating key performance indicators (KPIs), applying machine learning techniques to forecast business outcomes, and visualizing results in interactive Tableau dashboards.
Key Features
Data Import & Cleaning
Imported and cleaned Airbnb booking data from MySQL using the Snowflake connector, ensuring high-quality data for analysis and decision-making.
KPI Calculation
Calculated important KPIs like **Customer Retention Rate** and **Lead Time**, helping assess and optimize business performance.
Predictive Analytics & Forecasting
Forecasted revenue and expenses using Tableau’s machine learning functions, improving financial planning accuracy.
Interactive Tableau Dashboard
Developed a dynamic and interactive Tableau dashboard featuring:
Dynamic chart selection
Advanced charts and visualizations
Interactive tables and image maps
Enhanced data storytelling for business stakeholders
Feature Engineering & Machine Learning
Analyzed 30+ features using Python, SQL, and R, uncovering key drivers of perfect ratings and optimized listing strategies.
Built and evaluated six machine learning models including Linear Regression, Logistic Regression, Random Forest, Bagging, Ridge, and Lasso to predict performance.
Deployment
Deployed the final interactive dashboard on Tableau Server and Tableau Public, enabling real-time access to business insights and decision support.
Tools & Technologies
Python (NumPy, Pandas, Matplotlib, Scikit-learn, Faker)
SQL (MySQL)
Tableau (for interactive dashboards)
MySQL
Snowflake Connector
