What is jaccard similarity and how to calculate it?

What is jaccard similarity and how to calculate it?

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:


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'.

Relevant Projects

Build a Collaborative Filtering Recommender System in Python
Use the Amazon Reviews/Ratings dataset of 2 Million records to build a recommender system using memory-based collaborative filtering in Python.

Data Science Project - Instacart Market Basket Analysis
Data Science Project - Build a recommendation engine which will predict the products to be purchased by an Instacart consumer again.

Choosing the right Time Series Forecasting Methods
There are different time series forecasting methods to forecast stock price, demand etc. In this machine learning project, you will learn to determine which forecasting method to be used when and how to apply with time series forecasting example.

Predict Macro Economic Trends using Kaggle Financial Dataset
In this machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.

Build an Image Classifier for Plant Species Identification
In this machine learning project, we will use binary leaf images and extracted features, including shape, margin, and texture to accurately identify plant species using different benchmark classification techniques.

Identifying Product Bundles from Sales Data Using R Language
In this data science project in R, we are going to talk about subjective segmentation which is a clustering technique to find out product bundles in sales data.

Data Science Project-TalkingData AdTracking Fraud Detection
Machine Learning Project in R-Detect fraudulent click traffic for mobile app ads using R data science programming language.

Predict Credit Default | Give Me Some Credit Kaggle
In this data science project, you will predict borrowers chance of defaulting on credit loans by building a credit score prediction model.

Time Series Forecasting with LSTM Neural Network Python
Deep Learning Project- Learn to apply deep learning paradigm to forecast univariate time series data.

Sequence Classification with LSTM RNN in Python with Keras
In this project, we are going to work on Sequence to Sequence Prediction using IMDB Movie Review Dataset​ using Keras in Python.