The purpose of this assignment is to perform cluster analysis and analyze clusters in a data set to determine whether or not the information generated can be used to address a specific business problem.For this assignment, you will use the "Wholesale Customers" data set from the Topic Materials. Most data categories are self-explanatory. Clarifying notes are as follows.Fresh: Annual spending (in $1000s) on fresh productsMilk: Annual spending (in $1000s) on milk productsGrocery: Annual spending (in $1000s) on grocery productsFrozen: Annual spending (in $1000s) on frozen productsDetergents_Paper: Annual spending (in $1000s) on detergents and paper productsDelicatessen: Annual spending (in $1000s) on delicatessen productsClient_Type: Type of client – either HoReCa (Hotel/Restaurant/Café) or RetailRegion: Region of client – either Lisbon, Oporto, or OtherA wholesale distributor wants to understand the purchasing profiles of its clients. If a finite set of distinct profiles were defined, then a marketing strategy could be designed specifically for each set of similar clients. The company has compiled a data set that includes annual client spending on diverse product categories. You have been tasked with analyzing the data to determine what patterns emerge and how these patterns can be used to create specific client marketing profiles.Use k-means clustering to explore and analyze the data set by using only the quantitative variables to cluster the clients.Question 1: Explain the process you used to define the clusters such as the number of clusters formed, the specific variables used, etc. Include the "Cluster Sizes" and "Predictor Importance" outputs when submitting the answer.Question 2: Interpret the clusters with respect to the quantitative variables that were used in forming the clusters. Include the "Clusters" output when submitting the answer.Question 3: Discuss whether there is a pattern in the clusters with respect to the qualitative variables (i.e., Client_Type or Region). Include the charts illustrating these patterns when submitting the answer.Question 4: Provide an appropriate name for each cluster using any or all of the variables in the data set.Question 5: Based upon your analysis, what patterns emerged and how can these patterns be used to create specific client marketing profiles? Include discussion of the characteristics for each profile. Present your finding in the form of a 250-word executive summary that includes relevant data, charts, and tables.General Requirements:Submit the answers to Questions 1-4 and the executive summary as Word documents.APA format is not required, but solid academic writing is expected.This assignment uses a grading rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.

Database Design and AnalysisThe following database project will create an educational attainment “demand” forecast for the state of California for years greater than 2010. The demand forecast is the expected number of population who have obtained a certain level of education. The population is divided into age groups and education attainment is divided into different levels. The population of each group is estimated for each year up to year 2050. Implement the following steps to obtain and educational demand forecast for the state of California. The files can be downloaded below.Create a ca_pop schema in your MySQL database.Using your ca_pop schema, create an educational_attainment table which columns match the columns in the Excel spreadsheetca_pop_educational_attainment.csv.Using your ca_pop schema, create a pop_proj table which columns match the columns in the Excel spreadsheet pop_proj_1970_2050.csv.Using the data loading technique for a csv file you learned in Module 1, load the data in ca_pop_educational_attainment.csv into the table educational_attainment.Using the data loading technique for a csv file you learned in Module 1, load the data in pop_proj_1970_2050.csv into the table pop_proj.Write a query to select the total population in each age group.Use the query from Step 6 as a subquery to find each type of education attained by the population in that age group and the fraction of the population of that age group that has that educational attainment. Label the fraction column output as coefficient. For instance, the fraction of the population in age group 00 - 17 who has an education attainment of Bachelor's degree or higher is 0.0015, which is the coefficient.Create a demographics table from the SQL query from Step 7.Create a query on the pop_proj table which shows the population count by date_year and age.Use that query from Step 9 as a subquery and join it to the demographics table using the following case statement:demographics.age = case when temp_pop.age < 18 then '00 to 17' when temp_pop.age > 64 then '65 to 80+' else '18 to 64' end“temp_pop” is an alias for the subquery. Use the following calculation for the demand output:round(sum(temp_pop.total_pop * demographics.coefficient)) as demandOutput the demand grouped by year and education level.Write each query you used in Steps 1 – 10 in a text file (and submit results screenshots). If a query produced a result set, the list then first ten rows of each row set after the query.