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Implementing Logistic Regression with SciKit Learn

# Importing necessary libraries from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score # Load the Iris dataset iris = load_iris() X = iris.data y = iris.target # Split the data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Initialize the logistic regression model model = LogisticRegression(max_iter=1000) # Train the model on the training data model.fit(X_train, y_train) # Predict on the test data y_pred = model.predict(X_test) # Calculate accuracy accuracy = accuracy_score(y_test, y_pred) print(f"Accuracy of the logistic regression model: {accuracy:.2f}")

In this code snippet:

• We load the Iris dataset using SciKit Learn's load_iris function.
• Split the dataset into training and testing sets using train_test_split.
• Initialize a logistic regression model and train it on the training data.
• Predict on the test data and calculate the accuracy of the model using accuracy_score.

This example demonstrates a basic usage of SciKit Learn for training a machine learning model and evaluating its performance on a test set.

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