iris-perceptron-adaline-classification

Iris Classification with Perceptron and Adaline

This project implements binary classification on the Iris dataset using Perceptron and Adaline models.

📌 Overview

The goal of this project is to classify Iris Setosa and Iris Versicolor using two selected features: sepal length and petal length. The notebook demonstrates several fundamental machine learning concepts, including model training, feature standardization, decision boundary visualization, learning rate comparison, and model saving.

✨ Features

• Load Iris dataset from UCI Repository
• Select Setosa and Versicolor classes for binary classification
• Visualize data distribution using scatter plot
• Implement Perceptron from scratch
• Implement Adaline with Batch Gradient Descent
• Implement Adaline with Stochastic Gradient Descent
• Compare learning rate effects
• Apply feature standardization
• Plot decision boundaries
• Predict new sample data
• Save trained model using Pickle

🛠️ Tech Stack

• Python
• Jupyter Notebook
• NumPy
• Pandas
• Matplotlib
• Pickle

📊 Models Used

• Perceptron
• Adaline Gradient Descent
• Adaline Stochastic Gradient Descent

📁 Dataset

The dataset used in this project is the Iris dataset from the UCI Machine Learning Repository.

Streamlit

https://praktikum1-12427.streamlit.app/