Simple Statistical Analysis in machine learning with Python ( I am using Anaconda Spyder IDE)

Dataset: Retail Sales (Spreadsheet is included) 3 Tasks : I need working code that works for the following tasks:- 1) At least 3 INSIGHTS for Regression (Linear/Non-Linear whatever is applicable). Looking for complex and interesting insights. No simple one liner solutions please. I am using Anaconda Spyder IDE. Regression can be done on any column in the spreadsheet that is applicable. Once the task is done, I need just the code that works to find the right insights and visualize the data using matplotlib ). Ex: which client has made max purchases in the month of Jan, xxxx what are the predictions for next month based on sales so far. 2) 3 INSIGHTS for Clustering (K-means - Looking for complex and interesting insights. No simple one liner solutions please. Use your best knowledge and perform any column that is suitable. Once the task is done, I need just the code that works to find the right number of clusters and visualize the data using matplotlib). Analyze which columns would be better for clustering, by customer id, product, date ??? 3) 3 INSIGHTS for Classification (Decision Tree - Looking for complex and interesting insights. No simple one liner solutions please. Classify the data based on any column that makes sense. Once the task is done, I need just the code that works to find the right categories to classify the data and visualize the data using matplotlib). Analyze into how many categories the information can be classified. ** Only serious candidates reply please.