🌸 Iris Classification Project 🌸

Welcome to the Iris Classification Project! This project demonstrates the application of machine learning algorithms to classify Iris flowers into three species based on their physical characteristics. 🌼

📚 Dataset

The Iris dataset is a classic dataset in the field of machine learning and statistics. It consists of 150 samples, each with four features:

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

Each sample is classified into one of three species:

• Setosa
• Versicolor
• Virginica

🚀 Getting Started

Prerequisites

Make sure you have the following packages installed:

• Python 3.x
• scikit-learn
• pandas
• numpy
• matplotlib
• seaborn

You can install these dependencies using pip:

pip install scikit-learn pandas numpy matplotlib seaborn

📊 Visualization

Visualizing the data helps in understanding the distribution and relationships between different features. Here's an example of a pair plot:

💻 Usage

1. Load the Dataset: The Iris dataset can be loaded directly from scikit-learn.
2. Preprocess the Data: Encode categorical variables if necessary.
3. Split the Data: Divide the dataset into training and testing sets.
4. Train Models: Use different classifiers like Decision Tree, SVM, and KNN.
5. Evaluate Models: Measure the performance of each model using metrics like accuracy, precision, recall, and F1-score.
6. Visualize Results: Plot confusion matrices and decision boundaries.

🎯 Results

The performance of each classifier is evaluated and compared. Typically, models are assessed using accuracy, precision, recall, and F1-score. The results are visualized using confusion matrices and other plots.

Make sure to replace `pair_plot.png` with the filename of your actual visualization image.