Tutorial 31: Introduction to Logistic Regression Machine Learning Method with Scikit Learn and Pandas in Python

In this tutorial, we will cover how to use the Logistic Regression machine learning method in Python using Scikit Learn and Pandas. Logistic Regression is a classification algorithm used to predict the probability of a binary outcome based on one or more independent variables.

We will use the iris dataset for this tutorial, which is a commonly used dataset in machine learning. The iris dataset contains 150 samples of iris flowers, each with four features: sepal length, sepal width, petal length, and petal width. The target variable is the species of the iris flower, which can be one of three classes: setosa, versicolor, or virginica.

Let’s get started by importing the necessary libraries:

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix

Next, we will load the iris dataset into a Pandas DataFrame and split it into features and target variables:

iris = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=None)
X = iris.iloc[:, :-1]
y = iris.iloc[:, -1]

Now, we will split the data into training and testing sets using the train_test_split function from Scikit Learn:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

Next, we will create an instance of the Logistic Regression model and fit it to the training data:

model = LogisticRegression()
model.fit(X_train, y_train)

Now that our model is trained, we can make predictions on the test set and evaluate its performance using the classification_report and confusion_matrix functions:

y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))

The classification_report function displays precision, recall, F1-score, and support for each class in the target variable. The confusion_matrix function shows the number of true positives, true negatives, false positives, and false negatives for each class.

That’s it! You have successfully implemented the Logistic Regression algorithm using Scikit Learn and Pandas in Python. Logistic Regression is a powerful algorithm for binary classification tasks and is widely used in machine learning applications. Feel free to experiment with different datasets and hyperparameters to improve the performance of your model.