Forecasting sales is a difficult problem for every type of business, but it helps determine where a business should spend more on advertising and where it should cut spending. In this article, I will walk you through the task of Sales Prediction with Machine Learning using Python.
What is Sales Prediction?
Sales prediction means predicting how much of a product people will buy based on factors such as the amount you spend to advertise your product, the segment of people you advertise for, or the platform you are advertising on about your product.
Typically, a product’s sales are primarily dependent on how much you spend on advertising it, as the more people your product reaches, the more sales will increase as long as the quality of your product is good. Now in the section below, I will take you through a machine learning project on sales prediction using Python.
Sales Prediction using Python
Hope you now understand what sales forecasting is. Typically, a product and service-based business always need their Data Scientist to predict their future sales with every step they take to manipulate the cost of advertising their product. So let’s start the task of sales prediction with machine learning using Python. I’ll start this task by importing the necessary Python libraries and the dataset:
TV Radio Newspaper Sales 0 230.1 37.8 69.2 22.1 1 44.5 39.3 45.1 10.4 2 17.2 45.9 69.3 12.0 3 151.5 41.3 58.5 16.5 4 180.8 10.8 58.4 17.9
print(data.isnull().sum())
TV 0 Radio 0 Newspaper 0 Sales 0 dtype: int64
So this dataset does not contain any null values. Now let’s take a look at the correlation between features before we start training a machine learning model to predict future sales:

Now let’s prepare the data to fit into a machine learning model and then I will use a linear regression algorithm to train a sales prediction model using Python:
Predicted Sales 0 17.034772 1 20.409740 2 23.723989 3 9.272785 4 21.682719 5 12.569402 6 21.081195 7 8.690350 8 17.237013 9 16.666575 10 8.923965 11 8.481734 12 18.207512 13 8.067507 14 12.645510 15 14.931628 16 8.128146 17 17.898766 18 11.008806 19 20.478328 20 20.806318 21 12.598833 22 10.905183 23 22.388548 24 9.417961 25 7.925067 26 20.839085 27 13.815209 28 10.770809 29 7.926825 30 15.959474 31 10.634909 32 20.802920 33 10.434342 34 21.578475 35 21.183645 36 12.128218 37 22.809533 38 12.609928 39 6.464413
Summary
So this is how we can predict future sales of a product with machine learning. Forecasting sales is a difficult problem for every type of business but using Machine Learning makes it look easy. I hope you liked this article on the task of forecasting sales with machine learning using Python. Feel free to ask your valuable questions in the comments section below.