What is average stocked product value based on the below information: Product Quantity Value A 50 100 B 100 200 C 10 500

X is normally distributed random variable with mean of 3 and standard deviation of 2. Find the value of m such that (P >= m) = 0.95

You have trained an initial model to predict the propensity for charity supporters to leave a donation to charity in their will. Validation has shown that the model achieves 98% accuracy. Is this performance acceptable? Why or why not? If not, what steps could be taken to improve model performance?

Last year, your business conducted segmentation on their customers using first- and third-party data. Some of the third-party variables are no longer available. What approach(es) could we take to be able to continue using the solution?

We have provided two data tables, containing Supporter data and their donations to a charity. Using a language of your choice (e.g. R, Python, SQL) and the .csv files provided, write the code to create an example dataset that could be used as a starting point to train a model to predict a supporter’s propensity of giving a 2nd donation. You are not expected to complete extensive data exploration or cleaning.

For each piece of analysis work described below, specify the most appropriate statistical analysis or machine learning technique. Some items have more than one (equally) appropriate technique – please choose one to specify in your answer. Please include a brief description of any assumptions or limitations to your given answer. a) Predicting how much (individual) customers will spend in the next financial year. b) Modelling a time series of monthly sales data c) Identifying the key dimensions within a large number of variables relating to customer attitudes. d) Carrying out a segmentation to identify groups of customers with similar attitudes

You have been asked to generate a probability of customer being ‘”at risk of churn”. The client has provided data on 800,000 customers and their transactions. Outline the steps required as part of an end-to-end plan to identify “at-risk” customers, including approaches, considerations, limitations/risks, and further recommendations/next steps where appropriate.