Numpy Package

NumPy is a Python library used for numerical computations, including machine learning tasks. Here, we will discuss examples of some commonly used NumPy machine learning methods with accompanying Python code.

1. Linear Regression: Linear regression is a popular technique for modeling the relationship between a dependent variable and one or more independent variables. NumPy provides a convenient method for performing linear regression.

   import numpy as np
   from sklearn.linear_model import LinearRegression
   # Create a simple dataset
   X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
   y = np.dot(X, np.array([1, 2])) + 3
   # Train the linear regression model
   reg = LinearRegression().fit(X, y)
   # Print the coefficients
   print(reg.coef_)
   print(reg.intercept_)
   

2. Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that finds the principal components of a dataset. NumPy provides a method for performing PCA.

   import numpy as np
   from sklearn.decomposition import PCA
   # Create a dataset with three features
   X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
   # Fit PCA on the dataset
   pca = PCA(n_components=2)
   pca.fit(X)
   # Get the transformed data
   X_transformed = pca.transform(X)
   # Print the transformed data
   print(X_transformed)
   

3. K-Means Clustering: K-Means is a popular clustering algorithm that partitions data into K clusters. NumPy provides a method for performing K-Means clustering.

   import numpy as np
   from sklearn.cluster import KMeans
   # Create a dataset with two features
   X = np.array([[1, 2], [1, 4], [1, 0], [4, 2], [4, 4], [4, 0]])
   # Fit K-Means on the dataset
   kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
   # Get the cluster labels
   labels = kmeans.labels_
   # Print the cluster labels
   print(labels)
   

4. Support Vector Machines (SVM): SVM is a popular classification algorithm that finds the hyperplane that separates two classes. NumPy provides a method for performing SVM classification.

   import numpy as np
   from sklearn import svm
   # Create a dataset with two features and two classes
   X = np.array([[0, 0], [1, 1]])
   y = np.array([0, 1])
   # Fit SVM on the dataset
   clf = svm.SVC()
   clf.fit(X, y)
   # Predict the class of a new datapoint
   print(clf.predict([[2, 2]]))