Iris Species Classification with K-Nearest Neighbors (KNN)

Project Overview

This project uses the K-Nearest Neighbors (KNN) algorithm to classify iris flowers into one of three species based on four features:

• Sepal Length
• Sepal Width
• Petal Length
• Petal Width

The objective is to build a classification model that can predict the species of iris flowers using these features and evaluate its performance.

Installation

Requirements

• Python 3.7+
• Libraries:
  • numpy
  • pandas
  • matplotlib
  • seaborn
  • scikit-learn

Install dependencies using pip:

pip install numpy pandas matplotlib seaborn scikit-learn

Dataset

The dataset used is the popular Iris dataset, which is available in sklearn.datasets. It contains 150 samples, with 50 samples each for three species of iris flowers: Setosa, Versicolor, and Virginica.

Solution Approach

Load the Dataset: The dataset is loaded using load_iris from sklearn.datasets. Split the Dataset: Split the data into training and testing sets using train_test_split. Train the Model: Train the K-Nearest Neighbors (KNN) classifier on the training data. Evaluate the Model: Evaluate the classifier on the test set using metrics like accuracy, precision, recall, and F1 score. Visualize the Results: Visualize the model performance with a confusion matrix and scatter plots of feature distributions.

Evaluation Metrics

Accuracy: Measures overall correctness. Precision: Measures the accuracy of positive predictions. Recall: Measures the ability to find all positive instances. F1 Score: Harmonic mean of precision and recall, useful for imbalanced datasets.

Visualization

Confusion Matrix: Displays true vs. predicted labels, helping identify misclassifications.

Feature Scatter Plot: Provides a 2D visualization of sepal length and sepal width across species.

Results

The model achieves high accuracy and performs well across all metrics for this balanced dataset.