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Let's now understand the term:

*2. Entropy*You need not delve much into the mathematics behind, I just wanted to let you know the formula of Entropy. All these calculations automatically take place behind the screen. For example purpose only, let me calculate the

**Entropy**for

**Chennai :**

*So, the entropy for Chennai is 0.4690.***Entropy can be interpreted as degree of randomness or uncertainty or disturbance.**

Let's now logically deduce the concept :

When the data will be more skewed

In the nut shell we can say, more the skewness, better is predictability.=> It will be biased/skewed towards one of the categories

=> which means lesser will be the entropy/uncertainty/disturbance

=> and hence better would be the prediction outcome.

Now, calculate the entropy for all the cities.

We have learnt the calculation of category wise entropy. Now, next question would be

"

**How we would calculate the entropy at the variable level?"**

Solution is : Weighted average of all class/category

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