Here’s a brief tutorial for unsupervised learning using Python and scikit-learn library:
First, we need to import the necessary libraries and load the dataset:
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import pandas as pd from sklearn.cluster import KMeans # Load the dataset df = pd.read_csv('my_dataset.csv') |
Next, we need to prepare the data for clustering. This involves removing any unnecessary columns and scaling the data so that all features are on the same scale:
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# Remove unnecessary columns X = df.drop(['id', 'label'], axis=1) # Scale the data from sklearn.preprocessing import StandardScaler scaler = StandardScaler() X_scaled = scaler.fit_transform(X) |
Once the data is prepared, we can apply the KMeans clustering algorithm to the data. In this example, we will use KMeans to cluster the data into three groups:
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# Apply KMeans clustering algorithm kmeans = KMeans(n_clusters=3, random_state=42) kmeans.fit(X_scaled) # Get the cluster labels cluster_labels = kmeans.labels_ |
Finally, we can visualize the clusters using a scatter plot. In this example, we will plot the first two principal components of the data:
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# Visualize the clusters using a scatter plot import matplotlib.pyplot as plt from sklearn.decomposition import PCA pca = PCA(n_components=2) X_pca = pca.fit_transform(X_scaled) plt.scatter(X_pca[:,0], X_pca[:,1], c=cluster_labels) plt.xlabel('PC1') plt.ylabel('PC2') plt.show() |
Here’s the complete code:
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import pandas as pd from sklearn.cluster import KMeans from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA import matplotlib.pyplot as plt # Load the dataset df = pd.read_csv('my_dataset.csv') # Remove unnecessary columns X = df.drop(['id', 'label'], axis=1) # Scale the data scaler = StandardScaler() X_scaled = scaler.fit_transform(X) # Apply KMeans clustering algorithm kmeans = KMeans(n_clusters=3, random_state=42) kmeans.fit(X_scaled) # Get the cluster labels cluster_labels = kmeans.labels_ # Visualize the clusters using a scatter plot pca = PCA(n_components=2) X_pca = pca.fit_transform(X_scaled) plt.scatter(X_pca[:,0], X_pca[:,1], c=cluster_labels) plt.xlabel('PC1') plt.ylabel('PC2') plt.show() |
This script demonstrates a simple example of unsupervised learning using KMeans clustering to group data into three clusters, and visualizing the clusters using a scatter plot. Of course, the exact techniques and methods used for unsupervised learning will depend on the specific dataset and problem at hand.