What is jaccard similarity and how to calculate it?
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# What is jaccard similarity and how to calculate it?

This recipe explains what is jaccard similarity and how to calculate it

## Recipe Objective

Jaccard similarity can be defined to the size of intersection divided by the size of union of two sets. Hence it lies between values 0 & 1. In lay man's term, it is area of overlap/area of union.

So this recipe is a short example on what jaccard similarity is and how to calculate it. Let's get started.

## Step 1 - Setup the Data

``` x=['Ram','Shyam','Rohan'] y=['Ram','Rohan','Ganesh'] ```

Let us create a two list having two common elements.

## Step 2 - Defining Jaccard function

``` def jaccard(x,y): z=set(x).intersection(set(y)) a=float(len(z))/(len(x)+len(y)-len(z)) return a ```

We have used the mathematical property of jacccard function to defined the values to be returned if two list are passed into it as arguments.

## Step 3 - Calling function and printing results

``` z=jaccard(x,y) print(z) ```

First call the jaccard function and store the return value in any random variables. Now simply use print function to print new appended dataframe.

## Step 4 - Let's look at our dataset now

Once we run the above code snippet, we will see:

```0.5
```

For above example, we can observe that the area of intersection will be 2 elements and area of overlap will be 4 elements. So jacarrad similarity is 2/4 i.e. '0.5'.

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