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Now we will discuss a concept called seasonality in data.

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When we have time data and our data shows recurring patterns such as high is coming every three months.

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Other low coming in a particular season, such type of data is called seasonal data can have seasonality

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because of the weather.

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For example, every year.

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Ice cream sales are higher in summer and lower in winter.

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Or seasonality for tourism industry.

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Depends on holidays and vacations.

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Now, the variance in sales due to this season of the year may not be explained well by other factors

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of model and hence our model will not fit well.

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Therefore, it is advisable that we remove seasonality from the data.

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To do that, we usually find a correction factor, which we multiply with the data to get a normalized

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value.

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To calculate this factor, we can use this formula.

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M is equal to mean of a year divided by means of month.

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This will give us multiplication factor for each observation.

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Let me show you an example of how this is done.

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Here I have data off sales in the last three years and every year in the first six months, sales show

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a rise and then a dip.

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You can look at this graph from month one two, month three.

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There's a race.

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Month three and four.

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There's a flood.

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And after month food, there is a dip.

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After understanding the business context, if we are sure that this is due to the seasonal nature of

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sales, we find the factors to normalize these sales figures.

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If we use the formula shown earlier.

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I will get multiplication factors of each value.

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So this valley of 21.

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We'll get multiplication factor.

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By dividing mean of this year, by means of this month.

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But because the mean of these three years is almost similar.

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I have used population mean and divided by month, meaning I would suggest that you try doing it for

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each cell.

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So using population means divided by month, I mean, I have found these factors here, which I have

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multiplied with these sales figures.

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And below in this table, I have these multiplied values.

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So you can see if you multiply 21 with one point five two, you get thirty one point nine four.

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If you mind about twenty three with one point five two, you'll get thirty four point nine nine.

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So this is all this table is created using the multiplication factors.

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You can see that these old values are now in the same range and data is not seasonal.

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Not this data can be used for analysis and can be fed to our model.

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So this is all to move seasonality.