Input python code or issues

Sure! Here is a sample code for a simple 3-class classification using Python's scikit-learn library with Support Vector Machine (SVM) algorithm:

Python
1# Import necessary libraries
2from sklearn import datasets
3from sklearn.model_selection import train_test_split
4from sklearn.svm import SVC
5from sklearn.metrics import accuracy_score
6
7# Load the iris dataset (a popular dataset for classification)
8iris = datasets.load_iris()
9X = iris.data
10y = iris.target
11
12# Split data into training and testing sets
13X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
14
15# Train the Support Vector Machine (SVM) classifier
16clf = SVC(kernel='linear')
17clf.fit(X_train, y_train)
18
19# Make predictions on the test data
20predictions = clf.predict(X_test)
21
22# Calculate the accuracy of the model
23accuracy = accuracy_score(y_test, predictions)
24print("Accuracy:", accuracy)

In this code:

1. Load the Iris dataset.
2. Split the dataset into training and testing sets.
3. Use SVM algorithm for classification.
4. Train the model on the training data.
5. Make predictions on the test data.
6. Calculate and print the accuracy of the model.

This code performs a 3-class classification on the Iris dataset using SVM algorithm. The dataset is split into training and testing sets, and the accuracy of the model is calculated.