Hotel Booking Cancellation Analysis

Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn Explored and cleaned a comprehensive hotel booking dataset to identify key factors influencing guest cancellations. Performed Exploratory Data Analysis (EDA) to uncover trends related to lead time, deposit types, and customer segments. Engineered new features to capture complex behavioral patterns, such as calculating the total stay duration and categorizing lead times to enhance predictive power. Applied Principal Component Analysis (PCA) to reduce dataset dimensionality while retaining maximum variance, optimizing computational efficiency for subsequent modeling. Visualized booking patterns and cancellation rates across different hotel types (City vs. Resort hotels). Identified critical insights to help hotel management reduce revenue loss by predicting high-risk bookings