K-Nearest Neighbors in Python? - with practical example

K-Nearest Neighbors (KNN) is a simple and popular machine learning algorithm used for classification and regression tasks. It works by finding the K data points in the training set that are closest to the new data point, and then assigning the new data point the majority label of those K neighbors. Let's start with an example of using KNN for a classification task: Example 1: Suppose we have a dataset of flowers with features like petal length and width, and a target variable specifying the type of flower. We want to predict the type of a new flower based on its petal length and width using KNN. Step 1: Import necessary libraries and load the dataset. Step 2: Split the data into training and testing sets. Step 3: Create a KNN model with a specified value of K. Step 4: Fit the model on the training data. Step 5: Make predictions on the test data. Step 6: Evaluate the model performance using metrics like accuracy. 
# Example 1: KNN for classification
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Step 1
# Import necessary libraries and load the dataset
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
y = iris.target

# Step 2
# Split the 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)

# Step 3
# Create a KNN model with K=3
knn = KNeighborsClassifier(n_neighbors=3)

# Step 4
# Fit the model on the training data
knn.fit(X_train, y_train)

# Step 5
# Make predictions on the test data
y_pred = knn.predict(X_test)

# Step 6
# Evaluate the model performance
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

Now, let's move on to another example using KNN for a regression task: Example 2: Suppose we have a dataset with housing prices and features like the number of bedrooms and square footage. We want to predict the price of a new house based on these features using KNN. Step 1: Import necessary libraries and load the dataset. Step 2: Split the data into training and testing sets. Step 3: Create a KNN model with a specified value of K for regression. Step 4: Fit the model on the training data. Step 5: Make predictions on the test data. Step 6: Evaluate the model performance using regression metrics like mean squared error. 
# Example 2: KNN for regression
from sklearn.neighbors import KNeighborsRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# Step 1
# Import necessary libraries and load the dataset
import pandas as pd
from sklearn.datasets import load_boston
boston = load_boston()
X = boston.data
y = boston.target

# Step 2
# Split the 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)

# Step 3
# Create a KNN model with K=5 for regression
knn_reg = KNeighborsRegressor(n_neighbors=5)

# Step 4
# Fit the model on the training data
knn_reg.fit(X_train, y_train)

# Step 5
# Make predictions on the test data
y_pred = knn_reg.predict(X_test)

# Step 6
# Evaluate the model performance
mse = mean_squared_error(y_test, y_pred)
print("Mean Squared Error:", mse)

In these examples, we have demonstrated how to use K-Nearest Neighbors (KNN) algorithm for both classification and regression tasks in Python. By following the steps outlined in each example, you can apply KNN to your own datasets and make predictions based on the nearest neighbors.

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