The report should follow the CRISP-DM framework with considerations of the following when developing the report: i. Craft an introduction providing an overview of your data analysis, modelling results and findings, and business recommendations. Think of your introduction as an extended executive summary to be read by the senior management. ii. Analyse the data using statistics, data visualisations or machine learning tools to gain insights into customers' booking behaviours and characteristics at The Capri. iii. Formulate and discuss the modelling approaches to be adopted for your report. iv. Select and implement the appropriate modelling approaches. Evaluate the modelling results and appraise the modelling performance. v. Create the appropriate data analytical outputs from your data analysis and present your business insights. Craft a proposal to senior management on how you plan to address the issue of cancellations at The Capri. You are expected to utilise the appropriate statistical and machine learning techniques for your exploratory analysis and data modelling works and employ the appropriate performance evaluation metrics. Please provide sound justifications for your choice of exploratory, modelling and performance evaluation approaches. Your report will be assessed on the quality of its introduction, exploratory data analysis and insights, choice of modelling approaches, modelling implementation and insights, and your business proposal. As the report is meant to be read by the senior management, ensure that it is clear, logical, and accessible to a non-technical person. You may use bibliographical references as support for your arguments or relevant background work. Please ensure that the presentation of your results, citations and bibliographical references conform to the APA guidelines. You must provide evidence of data work. You may include such supporting evidence in the appendix. SPSS output and database should also be submitted.