Explain how to Plot Multinomial distribution with the help of seaborn?

This recipe explains how to Plot Multinomial distribution with the help of seaborn

Recipe Objective

Plot Multinomial distribution with the help of seaborn.

Multinomial distribution these are the generation of binomial distribution which describes outcomes of multi-nomial scenarios unlike binomial where scenarios must be only one of two. for e.g. human body blood group of a sample or population and outcome of dice roll.

Step 1 - Import the necessary libraries

from numpy import random import seaborn as sns import matplotlib.pyplot as plt

Step 2 - Take random samples

sample = random.multinomial(n=6, pvals=[1/6, 1/6, 1/6, 1/6, 1/6, 1/6]) sample

array([1, 1, 0, 4, 0, 0])

Step 3 - Plot the graph

sns.distplot(sample, hist= True) plt.show()

What Users are saying..

profile image

Jingwei Li

Graduate Research assistance at Stony Brook University
linkedin profile url

ProjectPro is an awesome platform that helps me learn much hands-on industrial experience with a step-by-step walkthrough of projects. There are two primary paths to learn: Data Science and Big Data.... Read More

Relevant Projects

End-to-End Snowflake Healthcare Analytics Project on AWS-2
In this AWS Snowflake project, you will build an end to end retraining pipeline by checking Data and Model Drift and learn how to redeploy the model if needed

MLOps Project to Deploy Resume Parser Model on Paperspace
In this MLOps project, you will learn how to deploy a Resume Parser Streamlit Application on Paperspace Private Cloud.

Tensorflow Transfer Learning Model for Image Classification
Image Classification Project - Build an Image Classification Model on a Dataset of T-Shirt Images for Binary Classification

Learn How to Build a Logistic Regression Model in PyTorch
In this Machine Learning Project, you will learn how to build a simple logistic regression model in PyTorch for customer churn prediction.

Hands-On Approach to Causal Inference in Machine Learning
In this Machine Learning Project, you will learn to implement various causal inference techniques in Python to determine, how effective the sprinkler is in making the grass wet.

Build CNN Image Classification Models for Real Time Prediction
Image Classification Project to build a CNN model in Python that can classify images into social security cards, driving licenses, and other key identity information.

Locality Sensitive Hashing Python Code for Look-Alike Modelling
In this deep learning project, you will find similar images (lookalikes) using deep learning and locality sensitive hashing to find customers who are most likely to click on an ad.

Credit Card Default Prediction using Machine learning techniques
In this data science project, you will predict borrowers chance of defaulting on credit loans by building a credit score prediction model.

Ensemble Machine Learning Project - All State Insurance Claims Severity Prediction
In this ensemble machine learning project, we will predict what kind of claims an insurance company will get. This is implemented in python using ensemble machine learning algorithms.

Deep Learning Project- Real-Time Fruit Detection using YOLOv4
In this deep learning project, you will learn to build an accurate, fast, and reliable real-time fruit detection system using the YOLOv4 object detection model for robotic harvesting platforms.