Sales forecasting using Machine Learning and Python Context: A sales forecast is a prediction of future sales revenue based on historical data, industry trends, and the status of the current sales pipeline. Businesses use the sales forecast to estimate weekly, monthly, quarterly, and annual sales totals. It is extremely important for a company to make an accurate sales forecast as it adds value across an organization and helps the different verticals to chalk out their future course of actions. Forecasting helps an organization to plan its sales operations by regions and provide valuable insights to the supply chain team regarding the procurement of goods and materials. An accurate sales forecast process has many benefits which include improved decision-making about the future and reduction of sales pipeline and forecast risks. Moreover, it helps to reduce the time spent in planning territory coverage and establish benchmarks that can be used to assess trends in the future.
SuperKartKart is an organization which owns a chain of supermarkets and food marts providing a wide range of products. They want to predict the future sales revenue of its different outlets so that they can strategize their sales operation across different tier cities and plan their inventory accordingly. To achieve this purpose, SuperKart has hired a data science firm, shared the sales records of its various outlets for the previous quarter and asked the firm to come up with a suitable model to predict the total sales of the stores for the upcoming quarter.
The data contains the different attributes of the various products and stores.The detailed data dictionary is given below.