This project implements a simple perceptron classifier to classify the Iris dataset.
The Iris dataset is a classic dataset in machine learning, containing measurements of various iris flowers. It consists of 150 samples of iris flowers, each with four features: sepal length, sepal width, petal length, and petal width. The flowers belong to three different species: setosa, versicolor, and virginica.
The goal of this project is to develop a perceptron classifier that can accurately classify iris flowers into their respective species based on their features.
The perceptron classifier is a type of linear classifier that learns to separate data points belonging to different classes by finding an optimal decision boundary in the feature space. It iteratively updates its weights and bias based on the misclassification of training examples until convergence.
- Clone the repository.
- Install the required dependencies using
pip install -r requirements.txt. - Run the
perceptron_iris.pyscript. - The script will train the perceptron model on the Iris dataset and evaluate its accuracy.
- Experiment with different hyperparameters and feature engineering techniques to improve the accuracy.
- Python 3.x
- NumPy
- scikit-learn
- Matplotlib
This project is licensed under the MIT License - see the LICENSE file for details.