IRIS CLASSIFICATION

The Iris Classification project applies various classification algorithms to the classic Iris dataset. We used models such as Logistic Regression, SVM, and Random Forests to classify iris species based on petal and sepal measurements. The project includes visualizations of the classification boundaries and performance metrics like accuracy, precision, and recall.

Visualizations

Static Images

Pairplot: Visual representation of the pairwise relationships between features in the Iris dataset.

KNeighborsClassifier Decision Boundary: Visualization of decision boundaries for K-Nearest Neighbors classifier.

Logistic Regression Decision Boundary: Decision boundaries created by the Logistic Regression model.

SVM Decision Boundary: Decision boundaries created by the Support Vector Machine model.

Animations

Training Process - Logistic Regression:

Training Process - Logistic Regression:

Training Process - Logistic Regression:

Training Process - Logistic Regression:

Training Process - Logistic Regression:

Training Process - K-Nearest Neighbors:

Training Process - K-Nearest Neighbors:

Training Process - K-Nearest Neighbors:

Training Process - K-Nearest Neighbors:

Training Process - K-Nearest Neighbors:

Training Process - K-Nearest Neighbors:

Training Process - SVM:

Training Process - SVM:

Training Process - SVM:

Training Process - SVM:

Training Process - SVM:

Training Process - SVM: