> For the complete documentation index, see [llms.txt](https://docs.akkio.com/akkio-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.akkio.com/akkio-docs/demo-models/demo-models/retail-sales-forecasting.md). # Retail Sales Forecasting A sales forecasting model with numeric output {% embed url="" %} The retail sales data dataset contains information about sales, marketing spend, and employee numbers across different regional stores. The dataset has 3000 rows and nine columns, including Store ID, Employee Number, Area, Date, Sales, Marketing Spend, Electronics Sales, Home Sales, and Clothes Sales. Forecasting sales is a crucial aspect of retail businesses, as it helps them plan their inventory, staffing, and marketing strategies. With Akkio, forecasting sales within this dataset can be done with ease. Akkio's machine learning algorithms can analyze the data and provide insights into the trends and patterns of sales across different regions and stores. This can help businesses make informed decisions about inventory, staffing, and marketing strategies. By using Akkio to forecast sales, businesses can gain a competitive edge by being able to predict future sales trends and adjust their strategies accordingly. This can help them stay ahead of the competition and maximize their profits. Additionally, Akkio's user-friendly interface makes it easy for businesses to input their data and get accurate forecasts quickly. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.akkio.com/akkio-docs/demo-models/demo-models/retail-sales-forecasting.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.