{"id":3416,"date":"2023-08-31T10:59:21","date_gmt":"2023-08-31T10:59:21","guid":{"rendered":"https:\/\/neuraldesigner.com\/blog\/retail-store-sales-forecasting\/"},"modified":"2025-09-26T16:38:03","modified_gmt":"2025-09-26T14:38:03","slug":"retail-store-sales-forecasting","status":"publish","type":"blog","link":"https:\/\/www.neuraldesigner.com\/blog\/retail-store-sales-forecasting\/","title":{"rendered":"Forecast sales of a retail store using machine learning"},"content":{"rendered":"

Sales forecasting<\/a> is a crucial task for managing a store, and machine learning can help identify the factors that influence sales in a retail store and estimate future sales.<\/p>\n

In this post, we use historical sales data of a drugstore chain to predict its sales up to one week in advance.<\/p>\n

You can follow the step-by-step construction of this predictive model by downloading the free trial of the data science and machine learning platform\u00a0Neural Designer<\/a>.<\/section>\n
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Contents<\/span><\/h3>\n<\/section>\n
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    \n
  1. Data analysis<\/a>.<\/li>\n
  2. Model training<\/a>.<\/li>\n
  3. Testing analysis<\/a>.<\/li>\n
  4. Model deployment<\/a>.<\/li>\n
  5. Conclusions<\/a>.<\/li>\n<\/ol>\n<\/section>\n
    \n

    Data analysis<\/h2>\n

    The first step\u00a0in the analysis is to examine the\u00a0dataset<\/a>, which contains <\/span>sales information from the drug stores.<\/p>\n

    Monthly sales<\/h3>\n

    The following time series chart illustrates the monthly sales volume.<\/p>\n

    \"Sales<\/p>\n

    As we can see, the number of sales peaks in July and more so in December.<\/p>\n

    The months with the fewest sales are January, May, and October.<\/p>\n

    Daily sales<\/h3>\n

    The following chart shows the distribution of sales throughout the month.<\/p>\n

    \"Sales<\/p>\n

    In this case, the days at the beginning and end of the month have higher activity.<\/p>\n

    Around the middle of the month, a smaller peak occurs.<\/p>\n

    Weekly sales<\/h3>\n

    It is also essential to examine the sales numbers by weekday.<\/p>\n

    The following time series chart shows the sales in these shops from Monday (1) to Sunday (7).<\/p>\n

    \"Sales<\/p>\n

    Monday is the preferred day for customers to make purchases in these retail shops.<\/p>\n

    During the rest of the week, the sales decrease from Monday to Thursday and increase on Friday.<\/p>\n

    On Sunday, there is a sharp decline in sales.<\/p>\n

    This is because most of the shops in this drugstore chain are closed on this day.<\/p>\n

    Variables<\/h2>\n

    The next step is to select and prepare the\u00a0variables<\/a> we will use.<\/p>\n

    The following list shows the\u00a0input variables<\/a>, also known as predictands, and the target variables<\/a>, also referred to as predictors.<\/p>\n

    Calendar variables<\/h3>\n