Unsupervised Learning for Data Preprocessing

Duration: 7 min

This module delves into the realm of unsupervised learning techniques, which are essential for data preprocessing. We will explore algorithms like K-Means, DBSCAN, Hierarchical Clustering, PCA, t-SNE, and Autoencoders. Understanding these methods is crucial for effectively preparing data for machine learning models, as they help in dimensionality reduction, feature extraction, and identifying hidden patterns within the data.

K-Means Clustering

K-Means is a popular clustering algorithm that partitions data into K distinct, non-overlapping subsets. The algorithm iteratively assigns each data point to the nearest cluster centroid and then recalculates the centroids. This process repeats until the centroids stabilize, indicating that the clusters have been optimally formed. K-Means is widely used for customer segmentation, image compression, and anomaly detection.

import numpy as np
from sklearn.cluster import KMeans

# Sample data
data = np.array([[1, 2], [1, 4], [1, 0],
                 [10, 2], [10, 4], [10, 0]])

# Initialize KMeans
kmeans = KMeans(n_clusters=2, random_state=0)

# Fit the model
kmeans.fit(data)

# Predict the cluster for each data point
print(kmeans.labels_)

Try it in Google Colab:

[1 1 1 0 0 0]

Principal Component Analysis (PCA)

PCA is a dimensionality reduction technique that transforms data into a lower-dimensional space by capturing the most significant variance in the data. It achieves this by computing the eigenvectors and eigenvalues of the data covariance matrix, which represent the principal components. PCA is commonly used for visualizing high-dimensional data, speeding up machine learning algorithms, and reducing overfitting.

from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
import numpy as np

# Sample data
data = np.array([[2, 3], [4, 5], [6, 7], [8, 9], [10, 11]])

# Standardize the data
scaler = StandardScaler()
data_scaled = scaler.fit_transform(data)

# Initialize PCA
pca = PCA(n_components=1)

# Fit and transform the data
data_pca = pca.fit_transform(data_scaled)

# Print the transformed data
print(data_pca)

💡 Tip: When applying PCA, always standardize your data first to ensure that each feature contributes equally to the analysis.