Iris Flower Classification Project

This project demonstrates a simple machine learning task of classifying Iris flowers into three species (setosa, versicolor, virginica) using various classification algorithms. It includes data preprocessing, exploratory data analysis (EDA), model selection, hyperparameter tuning, and evaluation.

Table of Contents

• Introduction
• Dataset
• Installation
• Usage
• Notebook
• Libraries Used
• License

Introduction

The Iris flower dataset is a classic dataset used in machine learning and statistics. It consists of 150 samples with 4 features (sepal length, sepal width, petal length, petal width) per sample. The goal is to predict the species of Iris flowers based on these features.

In this project, we use three different classification algorithms:

• Decision Tree
• k-Nearest Neighbors (k-NN)
• Logistic Regression

Each algorithm is tuned using GridSearchCV to find the best hyperparameters, and their performance is evaluated on a test set using accuracy metrics and classification reports.

Dataset

The dataset used in this project is the famous Iris dataset from scikit-learn. It is loaded using load_iris() function, which provides the feature data and target labels necessary for training and evaluation.

Installation

To run this project, follow these steps:

1. Clone the repository:

   git clone https://github.com/J3lly-Been/iris-flower-classification.git

2. Navigate to the project directory:

   cd iris-flower-classification

3. Install the required libraries:

   pip install -r requirements.txt

Usage

1. Open and run the iris_classification.ipynb notebook using Jupyter Notebook or JupyterLab:

   jupyter notebook iris_classification.ipynb

2. Follow the notebook instructions and execute each cell to see the results of each algorithm, including accuracy scores and classification reports.

Notebook

• iris_classification.ipynb: Jupyter Notebook containing the entire project code, including data loading, preprocessing, model training, hyperparameter tuning, evaluation, and visualization.

Libraries Used

• scikit-learn
• pandas
• seaborn
• matplotlib

License

This project is licensed under the MIT License. See the LICENSE file for details.