How to create Dot Plots in the StatsModels library?

This recipe describes how to create Dot Plots in the StatsModels library

Recipe Objective - How to create Dot Plots in the StatsModels library?

Dot plotting (also known as forest and blobbogram). It belongs to a class statsmodels.graphics.dotplots.dot_plot(points, intervals=None, lines=None, sections=None, styles=None, marker_props=None, line_props=None, split_names=None, section_order=None, line_order=None, stacked=False, styles_order=None, striped=False, horizontal=True, show_names='both', fmt_left_name=None, fmt_right_name=None, show_section_titles=None, ax=None)

Explore the Real-World Applications of Recommender Systems 

For more related projects -

https://www.dezyre.com/projects/data-science-projects/deep-learning-projects
https://www.dezyre.com/projects/data-science-projects/neural-network-projects

Example:

# Importing libraries
import numpy as np
import matplotlib.pyplot as plt
from statsmodels.graphics.dotplots import dot_plot

# Dot plot of 10 random numbers
dot_plot(np.random.randn(10))

# Dot plot of array
dot_plot(np.array([10,15,20,25,30]))

In this way, we can create Dot Plots in the StatsModels library.

What Users are saying..

profile image

Ameeruddin Mohammed

ETL (Abintio) developer at IBM
linkedin profile url

I come from a background in Marketing and Analytics and when I developed an interest in Machine Learning algorithms, I did multiple in-class courses from reputed institutions though I got good... Read More

Relevant Projects

Multilabel Classification Project for Predicting Shipment Modes
Multilabel Classification Project to build a machine learning model that predicts the appropriate mode of transport for each shipment, using a transport dataset with 2000 unique products. The project explores and compares four different approaches to multilabel classification, including naive independent models, classifier chains, natively multilabel models, and multilabel to multiclass approaches.

Predictive Analytics Project for Working Capital Optimization
In this Predictive Analytics Project, you will build a model to accurately forecast the timing of customer and supplier payments for optimizing working capital.

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

LLM Project to Build and Fine Tune a Large Language Model
In this LLM project for beginners, you will learn to build a knowledge-grounded chatbot using LLM's and learn how to fine tune it.

Learn to Build Generative Models Using PyTorch Autoencoders
In this deep learning project, you will learn how to build a Generative Model using Autoencoders in PyTorch

BERT Text Classification using DistilBERT and ALBERT Models
This Project Explains how to perform Text Classification using ALBERT and DistilBERT

Build Deep Autoencoders Model for Anomaly Detection in Python
In this deep learning project , you will build and deploy a deep autoencoders model using Flask.

Mastering A/B Testing: A Practical Guide for Production
In this A/B Testing for Machine Learning Project, you will gain hands-on experience in conducting A/B tests, analyzing statistical significance, and understanding the challenges of building a solution for A/B testing in a production environment.

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.

Predict Churn for a Telecom company using Logistic Regression
Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.