Scikit Learn Exercise

Fit a linear regression model to predict a target variable

from sklearn.linear_model import LinearRegression
import numpy as np

# Dummy dataset
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([1.2, 1.9, 3.1, 4.2, 5.1])

# Model fitting
model = LinearRegression()
model.fit(X, y)

# Prediction
prediction = model.predict(np.array([[6]]))
print("Prediction for input 6:", prediction)

Evaluate a classifier using a confusion matrix.

from sklearn.metrics import confusion_matrix

# True labels and predicted labels
y_true = [0, 1, 0, 1, 0, 1]
y_pred = [0, 1, 0, 0, 1, 1]

# Confusion Matrix
matrix = confusion_matrix(y_true, y_pred)
print("Confusion Matrix:\n", matrix)

Classify whether a number is greater than 3.

from sklearn.linear_model import LogisticRegression
import numpy as np

# Dummy dataset
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([0, 0, 0, 1, 1])  # 0 for ≤ 3, 1 for > 3

# Model fitting
model = LogisticRegression()
model.fit(X, y)

# Prediction
prediction = model.predict([[2], [4]])
print("Predictions:", prediction)

Train a random forest classifier.

from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification

# Generate dataset
X, y = make_classification(n_samples=100, n_features=4, random_state=42)

# Random Forest Classifier
clf = RandomForestClassifier()
clf.fit(X, y)

# Prediction
print("Prediction for first data point:", clf.predict([X[0]]))

Classify points based on a simple dataset.

from sklearn.neighbors import KNeighborsClassifier

# Dummy data
X = [[0], [1], [2], [3], [4], [5]]
y = [0, 0, 1, 1, 0, 0]  # 0 or 1 classes

# Model training
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X, y)

# Prediction
prediction = knn.predict([[2]])
print("Prediction for input 2:", prediction)

Cluster data points into 3 clusters using K-means.

from sklearn.cluster import KMeans
import numpy as np

# Dummy data
X = np.array([[1, 2], [1, 4], [1, 0],
              [4, 2], [4, 4], [4, 0]])

# K-means clustering
kmeans = KMeans(n_clusters=3, random_state=0)
kmeans.fit(X)
print("Cluster centers:\n", kmeans.cluster_centers_)
print("Labels for input points:", kmeans.labels_)

Build a simple decision tree classifier.

from sklearn.tree import DecisionTreeClassifier

# Dummy data
X = [[1], [2], [3], [4], [5], [6]]
y = [0, 0, 1, 1, 1, 0]

# Model training
tree = DecisionTreeClassifier()
tree.fit(X, y)

# Prediction
prediction = tree.predict([[3]])
print("Prediction for input 3:", prediction)

Reduce features from 4 to 2 using PCA.

from sklearn.decomposition import PCA
from sklearn.datasets import load_iris

# Load dataset
iris = load_iris()
X = iris.data

# PCA
pca = PCA(n_components=2)
X_reduced = pca.fit_transform(X)
print("Reduced feature data:\n", X_reduced[:5])

Scale the dataset using StandardScaler.

from sklearn.preprocessing import StandardScaler
import numpy as np

X = np.array([[1], [10], [100], [1000]])

# Scaling
scaler = StandardScaler()
scaled_X = scaler.fit_transform(X)
print("Scaled Data:\n", scaled_X)

Perform 5-fold cross-validation on an SVM classifier.

from sklearn import datasets
from sklearn.svm import SVC
from sklearn.model_selection import cross_val_score

# Load dataset
iris = datasets.load_iris()
X = iris.data
y = iris.target

# SVM Classifier
svm = SVC()

# Cross-validation
scores = cross_val_score(svm, X, y, cv=5)
print("Cross-validation scores:", scores)

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