Iris Flower Classification using K-Nearest Neighbors (KNN)

This task uses the K-Nearest Neighbors (KNN) algorithm to classify the Iris dataset. The Iris dataset is a classic dataset in machine learning and is often used for testing algorithms.

Dataset

The Iris dataset consists of 150 samples from each of three species of Iris flowers (Iris setosa, Iris virginica, and Iris versicolor). Four features were measured from each sample: the lengths and the widths of the sepals and petals.

• Number of samples: 150
• Number of features: 4
  • Sepal length in cm
  • Sepal width in cm
  • Petal length in cm
  • Petal width in cm
• Number of classes: 3
  • Iris setosa
  • Iris versicolor
  • Iris virginica
• Class distribution: 50 samples per class

Model

I have used the K-Nearest Neighbors (KNN) algorithm for this classification task. KNN is a simple and effective algorithm that classifies samples based on the majority class among the k nearest neighbors.

Training Method

1. Data Splitting: The dataset is split into training (70%) and test (30%) sets.
2. Model Training: A KNN model with k=3 is trained on the training set.
3. Evaluation: The model is evaluated using the accuracy score on the test set.

Results

The trained KNN model achieved an accuracy of 100.00% on the test set.

Requirements

• Python 3.x
• scikit-learn

Running the Code

To run the code, simply execute the script in a Python environment with the necessary libraries installed.

python classification.py