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Suppose we have a dataset of monthly sales for a retail store. We want to understand the trend of sales
over time.



Using a line chart, we can visualize the trend of monthly sales. The line chart shows that sales are
generally increasing over time, with some fluctuations. We can use this information to identify the peak
sales months and the months with lower sales.



To understand the relationship between sales and price, we can create a scatter plot. The scatter plot
shows that there is a negative correlation between price and sales, meaning that higher-priced items
tend to have lower sales. We can use this information to adjust our pricing strategy and improve sales.



Using if-then rules, we can create a pricing algorithm that adjusts prices based on sales. For example, if
sales for a particular item are below a certain threshold, we can automatically lower the price to attract
more customers. Conversely, if sales are above a certain threshold, we can raise the price to maximize
profits.



Overall, by decoding patterns and rules in our sales data, we can make informed decisions that improve
our bottom line.