For each specific quesion, you must write 1 and only 1 query that answers that specific question. The results of your query must be displayed in a Pandas dataframe as demonstrated in the labs

All output should be sorted.

For store name, use the city name the store is located in.

When sorting by store name, sort in alphabetical order.

For month, use the full month name: January, February, March, etc.

When sorting by month, sort in calendar order: January, February, March, etc. You may find it useful to include a month number to help with the sort. It's ok to leave it in the output.

For day of week, use the full day of week name: Sunday, Monday, Tuesday, etc.

When sorting by day of week, sort in day of week order: Sunday, Monday, Tuesday, etc. You may find it usefule to include a day of week number to help with the sort. It's ok to leave it in the output.

1.1.1 Total Sales as a Dollar Amount for all of AGM

1.1.2 Total Sales as a Dollar Amount by Store

1.1.3 Total Sales as a Dollar Amount by Month

1.1.4 Total Sales as a Dollar Amount by Store and Month

1.1.5 Total Sales as a Dollar Amount by Day of Week

1.1.6 Total Sales as a Dollar Amount by Store and Day of Week

1.1.7 Total Number of Sales for all of AGM

1.1.8 Total Number of Sales by Store

1.1.9 Average Dollar Amount per Sale

1.1.10 Average Dollar Amount per Sale by Store

1.1.11 The Executives have also asked you to provide your best example of a data visualization for one of the above queries

1.2.1 Total Number of Customers for all of AGM

1.2.2 Total Number of Customers by Store

1.2.3 Total Number of Customers by Distance from Store

1.2.4 List of Customers who have signed up but not bought anything

1.2.5 What is the percentage of customers per population at the zip code level?

1.2.6 What is the percentage of customers per population at the city level

1.2.7 The Executives have also asked you to provide your best example of a data visualization for one of the above queries

1.3.1 How many meals were purchased for all of AGM?

1.3.2 How many meals were purchased for all of AGM by meal?

1.3.3 How many meals were purchased by store and by meal?

1.3.4 How many meals were purchased by month?

1.3.5 How many meals were purchased by month and meal?

1.3.6 How many meals were purchased by day of week and meal?

1.3.7 Average number of meals per sale for all of AGM

1.3.8 Average number of meals per sale by store

1.3.9 The Executives have also asked you to provide your best example of a data visualization for one of the above queries

1.4

How do holidays affect the sales, considering both the actual holiday, and the days before and after the holiday?

Create an executive summary explaining how holidays have affected sales.

You must support your summary with data, in the form of output of queries, data visualization, etc. There is a 1 query minimum.

1.5

The executives want you to come up with a high level design of a model, in the form of written criteria, to determine who the best customers are.

You do NOT have to code the model.

You do NOT have to give an actual list of best customers.

Create an executive summary explaining your model. You must support your summary with data, in the form of output of queries, data visualization, etc. There is a 1 query minimum.

1.6

The executives would like your best recommendation for the business.

Create an executive summary giving and explaining your best recommendation for the business.

You must support your summary with data, in the form of output of queries, data visualization, etc. There is a 1 query minimum.

Sales Database Physical Data Model (DM) in Entity-Relationship Diagram (ERD) notation and Data Dictionary Sales Database DM in ERD (Primary Dataset) stores sales store_id (PK) street city state zip latitude longitude products product_id (PK) description line items store_id (PK) (FK) sale_id (PK) (FK) line_item_id (PK) product_id (FK) quantity store_id (PK) (FK) sale_id (PK) customer_id (FK) sale_date total amount holidays holiday_date (PK) description closed_flag customers customer_id (PK) first_name last_name street city state zip closest_store_id (FK) distance