This repository contains a Python script that implements a Support Vector Machine (SVM) classifier for the famous Iris dataset. The SVM classifier is built from scratch using Python and NumPy.
Make sure you have the following libraries installed:
- Pandas
- NumPy
- Scikit-learn
- Seaborn
You can install these libraries using pip:
pip install pandas numpy scikit-learn seaborn
The Iris dataset is loaded directly from the provided CSV file Iris.csv. The dataset contains 150 samples of iris flowers, each with four features: sepal length, sepal width, petal length, and petal width. The target variable is the species of iris, which has three classes: Iris-setosa, Iris-versicolor, and Iris-virginica.
Before training the classifier, a pairplot visualization is created using Seaborn. This visualization helps to understand the relationships between different features and how they can be used for classification.
The SVM classifier is implemented as a Python class SVM. The class contains methods for fitting the model and making predictions. The hyperparameters like learning rate, regularization parameter, and number of iterations can be adjusted during initialization.
The dataset is split into training and testing sets using the train_test_split function from scikit-learn. The SVM classifier is then trained on the training set and tested on the testing set.
You can use this script by simply running it in your Python environment. Make sure the Iris.csv file is in the same directory as the script or provide the correct path to the file.
python svm_classifier.pyThe script outputs the predictions made by the SVM classifier and the actual labels from the testing set. It also prints the accuracy of the classification.
- [Link to Medium article](#) - Share your Medium article discussing this project and your insights.
This script is authored by [Muhammad Ali Ahson].
Feel free to reach out with any questions or suggestions!