SDG-Classifier

AIM:

To write a program to predict the type of species of the Iris flower using the SGD Classifier.

Equipments Required:

1. Hardware – PCs
2. Anaconda – Python 3.7 Installation / Jupyter notebook

Algorithm

1. Import Necessary Libraries and Load Data
2. Split Dataset into Training and Testing Sets
3. Train the Model Using Stochastic Gradient Descent (SGD)
4. Make Predictions and Evaluate Accuracy
5. Generate Confusion Matrix

Program:

/*
Program to implement the prediction of iris species using SGD Classifier.
*/
 import pandas as pd
 from sklearn.datasets import load_iris
 from sklearn.linear_model import SGDClassifier
 from sklearn.model_selection import train_test_split
 from sklearn.metrics import accuracy_score, confusion_matrix
 import matplotlib.pyplot as plt
 import seaborn as sns
 # Load the Iris dataset
 iris = load_iris()
 # Create a Pandas DataFrame
 df = pd.DataFrame(data=iris.data, columns=iris.feature_names)
 df['target'] = iris.target
 # Display the first few rows of the dataset
 print(df.head())
 # Split the data into features (X) and target (y)
 X = df.drop('target', axis=1)
 y = df['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)
 # Create an SGD classifier with default parameters
 sgd_clf = SGDClassifier(max_iter=1000, tol=1e-3)
 # Train the classifier on the training data
 sgd_clf.fit(X_train, y_train)
 # Make predictions on the testing data
 y_pred = sgd_clf.predict(X_test)
 # Evaluate the classifier's accuracy
 accuracy = accuracy_score(y_test, y_pred)
 print(f"Accuracy: {accuracy:.3f}")
 # Calculate the confusion matrix
 cm = confusion_matrix(y_test, y_pred)
 print("Confusion Matrix:")
 print(cm)

Output:

Result:

Thus, the program to implement the prediction of the Iris species using SGD Classifier is written and verified using Python programming.