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Case Study: Predictive Analytics in Hotel Booking Management Objective:- This case study aims to equip you with practical skills in data science, focusing on predicting customer behaviors and booking cancellations in the hotel industry. Applied EDA, KNN, and Decision Tree algorithms, and learned to handle class imbalances using SMOTE. Data Overview:- The dataset from 'INN Hotels,' contains various features related to hotel bookings. I have analyzed this data to uncover insights and predict booking cancellations.

1. Data Cleaning and Preprocessing -Inspect the dataset for anomalies and missing values. -Apply appropriate preprocessing techniques to prepare the data for analysis.
2. Exploratory Data Analysis (EDA) -Conducted a thorough analysis using statistical summaries and data visualizations. -Explored relationships between different variables and understood booking trends.
3. Predictive Modeling -Implemented K-nearest neighbors (KNN) and Decision Tree models to predict booking cancellations. -Evaluated and fine-tuned your models to achieve the best performance.
4. Handling Class Imbalance with SMOTE -Understood how a class imbalance in your dataset can skew the results. -Applied SMOTE to balance the dataset and retrain your models. -Compare the performance of your models before and after applying SMOTE.