Logistic Regression is a supervised machine learning algorithm used for classification tasks. It predicts the probability that a data instance belongs to a particular class by applying the sigmoid function to a linear combination of input features. Unlike linear regression, which predicts continuous values, logistic regression is designed to classify data into categories such as Yes/No, True/False, or different class labels. It is widely used in applications such as disease prediction, spam email detection, customer churn prediction, and sentiment analysis because of its simplicity, efficiency, and interpretability. For implementing Logistic Regression in Python, the Iris dataset is chosen. The Iris dataset is one of the most popular datasets for classification problems. It consists of 150 samples of iris flowers belonging to three species: Setosa, Versicolor, and Virginica. Each sample contains four numerical features: sepal length, sepal width, petal length, and petal width. These measurements are used to classify the flower into its corresponding species. The implementation uses the Scikit-learn library, which provides efficient machine learning algorithms and utilities for data preprocessing, model training,