Developing a Neural Network Classification Model

AIM

To develop a neural network classification model for the given dataset.

THEORY

The Iris dataset consists of 150 samples from three species of iris flowers (Iris setosa, Iris versicolor, and Iris virginica). Each sample has four features: sepal length, sepal width, petal length, and petal width. The goal is to build a neural network model that can classify a given iris flower into one of these three species based on the provided features.

Neural Network Model

Include the neural network model diagram.

DESIGN STEPS

STEP 1:

Write your own steps

STEP 2:

STEP 3:

STEP 4:

STEP 5:

STEP 6:

PROGRAM

Name:

Register Number:

class IrisClassifier(nn.Module):
    def __init__(self, input_size):
        super(IrisClassifier, self).__init__()
        #Include your code here

    def forward(self, x):
        #Include your code here



# Initialize the Model, Loss Function, and Optimizer

def train_model(model, train_loader, criterion, optimizer, epochs):
    #Include your code here

Dataset Information

Include screenshot of the dataset.

OUTPUT

Confusion Matrix

Include confusion matrix here

Classification Report

Include classification report here

New Sample Data Prediction

Include your sample input and output here

RESULT

Include your result here