Need a video tutorial showing in steps email classification process using ML learning in R language

The video tutorial needed must show in detailed steps how the process of email classification using machine learning in R language can be implemented. The project is a POC about classifying emails extracted from outlook based on their content and labels (categories). The main goal of the project is to automate the process of email classification in a general mail center (hub), where hundreds of inquiries and questions from audience are received on daily basis. currently, emails are classified into categories manually which is costing effort and time and hence, assigned to a subfolder in the inbox based on its content; so that the specialized department/section can review it and take action based on it. A sample of the dataset will be provided (3-5 emails only) since it is not an open source data, just to give an idea about the nature of the dataset which needs some cleansing and structuring. The dataset contains emails in both english and arabic language, and the labels are also available. Looking for a data scientist/programmer who is familiar with R and can create a video tutorial showing the following steps: 1) preprocessing and cleansing of the dataset since it is not structured, in R. 2) Statistical characteristics of the dataset in R. 3) Visualization graphs of the dataset in R. 4) Word cloud of frequent words within the dataset in R. 5) Applying classification algorithms and measuring the performance of each algorithm in R (decision trees, k-nearest neighbors, naive babes..etc) 6) Training and testing the solution model in R using the dataset.