The Prompt
Given the attached spreadsheet of recurring services that my company provides to my customers, can you create a forecasting visualization of man hours required to complete the services for the next 12 months? Base the date on the “due_next” field and the man hour estimate on the “estimated_duration” field in the data.The Output
The spreadsheet contains a wide range of columns (140 in total), including the fields id, ref_number, company, company_street, company_city, company_state, company_postalcode, store_number, location_name, and many more, with some fields relevant to your request such as estimated_duration. However, the due_next field you mentioned for basing the forecast on does not appear in the first few columns displayed. I’ll need to locate this field and the estimated_duration field specifically to proceed with creating the forecasting visualization. Let’s identify and extract the necessary data for the due_next and estimated_duration fields from the dataset. [AI stops and thinks for a bit here…] The due_next and estimated_duration fields are indeed present in the dataset. Now, I’ll proceed to filter and prepare the data for the forecasting visualization. This will involve:- Extracting only the relevant fields: due_next and estimated_duration.
- Ensuring that the due_next field is in a proper date format and that estimated_duration is in a numeric format.
- Aggregating the data by month to calculate the total estimated man hours required for each month in the next 12 months.
