In the project focused on hotel booking status prediction using predictive analytics, I conducted a case study to analyze and predict the outcome of hotel bookings. The dataset was divided into a training set and a test set, containing various variables related to hotel bookings, such as customer demographics, booking details, and historical patterns.
To initiate the project, I carefully examined the dataset, ensuring its quality and completeness. I performed data preprocessing tasks, including handling missing values, feature selection, and transformation, to prepare the data for analysis. By dividing the dataset into train and test sets, I ensured the integrity of the analysis and evaluation processes.
Next, I applied predictive analytics techniques to build models that could accurately predict the booking status of hotels based on the given features. This involved selecting suitable algorithms, training the models on the training set, and evaluating their performance using appropriate metrics like accuracy, precision, and recall. Through this analysis, I aimed to provide valuable insights and predictions that could assist hotel management in optimizing their booking strategies and improving customer satisfaction.