Lecture 46:- K-NN Code

here's a basic example of how to implement the K-Nearest Neighbors (K-NN) algorithm for a classification task using Python and the scikit-learn library:

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import numpy as np from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import accuracy_score # Generate mock data np.random.seed(0) X = np.random.rand(100, 2) # 100 samples, 2 features y = (X[:, 0] + X[:, 1] > 1).astype(int) # Create binary labels based on the sum of features # Split data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Create and train the K-NN classifier k = 3 # Number of neighbors model = KNeighborsClassifier(n_neighbors=k) model.fit(X_train, y_train) # Make predictions on the test set y_pred = model.predict(X_test) # Calculate accuracy accuracy = accuracy_score(y_test, y_pred) print(f"Accuracy: {accuracy:.2f}")

This example demonstrates the following steps:

1. Import necessary libraries (numpy, scikit-learn).
2. Generate mock data with two features and binary labels.
3. Split the data into training and testing sets.
4. Create a KNeighborsClassifier model from scikit-learn with the desired number of neighbors (k).
5. Train the model on the training data.
6. Make predictions on the test set.
7. Calculate and print the accuracy of the model.

Keep in mind that this is a basic example to help you understand the implementation of K-NN. In practice, you might need to preprocess your data, tune the value of k, and handle other aspects of model evaluation and selection.

Additionally, if you're working with a regression task, you can use KNeighborsRegressor from scikit-learn in a similar manner.