Iris Flower Classification – Machine Learning Project

This repository contains multiple implementations of a machine learning model developed to classify Iris flower species based on their physical measurements. It is part of a series of projects completed during a Data Science internship at CodSoft.

Project Overview

The goal of this project is to build a supervised machine learning model that can accurately classify Iris flowers into one of three species:

• Setosa
• Versicolor
• Virginica

These species are distinguishable by measurements of their sepal length, sepal width, petal length, and petal width. This classification task serves as a foundational introduction to supervised learning and is a widely used benchmark in the field of machine learning.

To strengthen my learning and enhance my understanding of real-world applications, I implemented this model using multiple technologies and interfaces:

1. Jupyter Notebook (Core ML development)
2. Flask Web Application (Python backend with RESTful interaction)
3. Streamlit Application (Rapid prototyping and interactive visualization)
4. HTML + JavaScript Web App (Client-side model implementation)

Each version serves as an independent deployment approach and reflects my intent to explore the different ways machine learning can be integrated into user-facing applications.

Dataset

• Source: UCI Machine Learning Repository – Iris Dataset

• Features:

  • Sepal length (cm)
  • Sepal width (cm)
  • Petal length (cm)
  • Petal width (cm)

• Target: Species (setosa, versicolor, virginica)

Project Structure

.
├── Iris classification model/                      # Jupyter Notebook model
├── Flask iris classification prediction web app/   # Flask-based deployment
├── Streamlit Iris dataset classification .../      # Streamlit UI
├── Web app/                                        # HTML + JavaScript implementation
├── .gitignore
└── README.md

Workflow

Data Preprocessing

• Dataset loaded into Pandas DataFrame
• Checked for null or duplicate entries
• Features and labels separated for training

Model Building

• Algorithms tested:

  • K-Nearest Neighbors
  • Decision Tree
  • Gaussian Naive Bayes

• Model evaluation using accuracy scores and confusion matrix

Deployment

• Built and deployed applications using different tools:

  • Flask: Backend API in Python
  • Streamlit: Interactive app with real-time predictions
  • HTML + JavaScript: Model implemented for browser-based predictions
  • Jupyter Notebook: Full exploratory development and analysis

Learning and Motivation

The foundational implementation was entirely coded by me in Jupyter Notebook, where I handled the data analysis, feature selection, model training, and evaluation.

Once I had the core logic in place, I expanded the project by experimenting with deployment and interactivity. I explored frameworks like Flask and Streamlit, and even attempted client-side machine learning using HTML + JavaScript. This hands-on experimentation allowed me to better understand the end-to-end journey of an ML project—from raw data to a deployable application.

Throughout this process, I also leveraged AI-based coding tools for suggestions, automation, and error resolution. These tools significantly enhanced the learning curve and helped me explore concepts more efficiently. My goal with this project was to learn by doing, make mistakes, iterate fast, and understand the practical nuances of deploying machine learning solutions.

Technologies Used

• Python 3.x
• Jupyter Notebook
• Pandas, NumPy
• Scikit-learn
• Matplotlib, Seaborn
• Flask
• Streamlit
• HTML, CSS, JavaScript

Acknowledgments

This is Task 3 of the Data Science Internship at CodSoft. Development was supported by self-study, community tutorials, and AI-assisted development platforms.