EDA on IRIS Dataset using Logistic Regression Model

The video is about Data Visualization of IRIS Dataset and making predictions using Logistic Regression Model. Exploratory Data Analysis (EDA) is the approach of analyzing the critical process of performing an initial investigation of the dataset to discover patterns and anomalies, and form hypotheses based on our understanding of the datasets. It is used to analyze data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods.

In this video, we are going to see how to Perform EDA Using IRIS Dataset. IRIS dataset has three different types of irises (Setosa, Versicolour, and Virginica) petal and sepal length, stored in a 150x4 numpy.ndarray. And Deeply Analyse the EDA with these datasets. While Performing the EDA with iris we will start with importing the required library and then import the iris dataset, we will use data visualization to analyse its pattern, and then we will create a machine learning model with logistic regression using sklearn modules and then apply the machine learning model to the machine.

Related article: https://www.geeksforgeeks.org/what-is-exploratory-data-analysis https://www.geeksforgeeks.org/exploratory-data-analysis-in-python/