14 — Exploring Logistic Regression with the “Iris” Dataset
<p>Welcome to another exciting chapter of the Data Science 2-Minutes Series! In this chapter, we’re going to dive into the world of Logistic Regression using the classic “Iris” dataset. Get ready for a fun and informative read on classification!</p>
<h1>Step 1: Data Wrangling</h1>
<p>We start by loading the Iris dataset and preparing it for our analysis. Think of it as getting the stage ready for our rockstar performance.</p>
<pre>
from sklearn.datasets import load_iris
import pandas as pd
data = load_iris()
df = pd.DataFrame(data.data, columns=data.feature_names)
# Add the target column
df['species'] = data.target_names[data.target]</pre>
<p><img alt="" src="https://miro.medium.com/v2/resize:fit:700/1*S4c3mHkcAtUHfhi_LkOuDw.png" style="height:398px; width:700px" /></p>
<h1>Step 2: The Logistic Regression Show</h1>
<p>Now, it’s showtime! We create our Logistic Regression model to predict the iris species. It’s like having the perfect instruments for a rock concert.</p>
<p><a href="https://medium.com/@redwaneaitouammi/14-exploring-logistic-regression-with-the-iris-dataset-6c0b28cf4df0"><strong>Click Here</strong></a></p>