How to Build Your First KNN Python Model in scikit-learn (K Nearest Neighbors)

If you’re new to machine learning and want to build your first KNN (K Nearest Neighbors) Python model using the scikit-learn library, then you’ve come to the right place. KNN is a simple and powerful algorithm that can be used for both classification and regression tasks. In this article, we’ll walk you through the steps to build your first KNN model in Python.

Step 1: Install scikit-learn

The first step is to install scikit-learn, which is a popular machine learning library in Python. You can install it using pip by running the following command in your terminal:

pip install scikit-learn

Step 2: Import the necessary libraries

Next, you need to import the necessary libraries in your Python code. This includes scikit-learn for building the KNN model, as well as other libraries for data manipulation and visualization.

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
    

Step 3: Load and prepare the data

Now, you need to load your dataset and prepare it for training the KNN model. This may involve cleaning the data, handling missing values, and splitting the dataset into training and testing sets.

# Load the dataset
data = pd.read_csv('dataset.csv')

# Split the dataset into features and target variable
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values

# Split the dataset 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)

# Feature scaling
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
    

Step 4: Build and train the KNN model

With the data prepared, you can now build and train the KNN model using scikit-learn’s KNeighborsClassifier class. You’ll need to choose the number of neighbors (k) and fit the model to the training data.

# Build the KNN model
k = 3
knn = KNeighborsClassifier(n_neighbors=k)

# Train the model
knn.fit(X_train, y_train)
    

Step 5: Make predictions and evaluate the model

Finally, you can use the trained KNN model to make predictions on the testing data and evaluate its performance using accuracy or other metrics.

# Make predictions
y_pred = knn.predict(X_test)

# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy: {accuracy}')
    

And there you have it – your first KNN Python model built using scikit-learn! This is just a basic introduction to KNN, and there are many more advanced techniques and concepts to explore. But this should give you a solid foundation to start building and experimenting with machine learning models.