SGD-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:

Step 1: Start the program.

STEP 2: Import Necessary Libraries and Load Data.

Step 3: Split Dataset into Training and Testing Sets.

Step 4: Train the Model Using Stochastic Gradient Descent (SGD).

Step 5: Make Predictions and Evaluate Accuracy.

Step 6: Generate Confusion Matrix.

STEP 7: Stop the program.

Program:

/*
Program to implement the prediction of iris species using SGD Classifier.
Developed by: sanjai R
RegisterNumber: 212223040180
*/

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

#print the first 5 values
print(df.head())

#split the data into features (x) and(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 thr training data
sgd_clf.fit(X_train,Y_train)

#make predictions on the testing data
y_pred=sgd_clf.predict(X_test)

print(f"Accuracy:{accuracy:.3f}")

#calculate the confusion matrix
cf=confusion_matrix(Y_test, y_pred)
print("Confusion Matrix")
print(cf)
*/

Output:

Result:

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