Ex.No: 13 Learning – Use Supervised Learning

DATE:

REGISTER NUMBER : 212221220062

AIM:

To write a program to train the classifier for RGB color predection

Algorithm:

Step 1: Import Necessary Libraries

Step 2: Define or Load the Dataset

Step 3: Convert Data to Arrays

Step 4: Split the Dataset

Step 5: Choose a Classifier

Step 6: Train the Classifier

Step 7: Take User Input

Step 8: Convert User Input to RGB

Step 9: Make Prediction

Step 10: Display Prediction

Program:

Import necessary libraries from sklearn.tree import DecisionTreeClassifier

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score

import numpy as np

Generate a sample dataset (replace this with your actual dataset) Here, we're using RGB values as features

data = { 'red': {'rgb': [255, 0, 0], 'label': 'red'}, 'green': {'rgb': [0, 255, 0], 'label': 'green'}, 'blue': {'rgb': [0, 0, 255], 'label': 'blue'}, 'yellow': {'rgb': [255, 255, 0], 'label': 'yellow'}, 'purple': {'rgb': [128, 0, 128], 'label': 'purple'}, # Add more samples as needed }

Convert data into arrays colors = list(data.keys())

rgb_values = np.array([data[color]['rgb'] for color in colors])

labels = np.array([data[color]['label'] for color in colors])

Split the data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(rgb_values, labels, test_size=0.2, random_state=42)

Choose a classifier (Decision Tree in this example)

classifier = DecisionTreeClassifier()

Train the classifier

classifier.fit(X_train, y_train)

Make predictions on the test set

predictions = classifier.predict(X_test)

Evaluate the accuracy of the classifier

accuracy = accuracy_score(y_test, predictions)

print(f"Accuracy: {accuracy}")

Example:

Predict the color of a new RGB value

new_rgb_value = np.array([[100, 150, 200]])

predicted_color = classifier.predict(new_rgb_value)

print(f"Predicted color for RGB {new_rgb_value}: {predicted_color}")

Output:

Result:

Thus the system was trained successfully and the prediction was carried out.