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3

 import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.datasets import load_iris from sklearn.decomposition import PCA # Load the Iris dataset iris = load_iris() data = iris.data labels = iris.target label_names = iris.target_names # Reduce dimensionality to 2 using PCA pca = PCA(n_components=2) reduced_data = pca.fit_transform(data) # Create DataFrame for visualization df_pca = pd.DataFrame(reduced_data, columns=['PC1', 'PC2']) df_pca['Label'] = labels # Plot PCA result plt.figure(figsize=(8, 6)) colors = ['r', 'g', 'b'] for i, label in enumerate(np.unique(labels)): subset = df_pca[df_pca['Label'] == label] plt.scatter(subset['PC1'], subset['PC2'], color=colors[i], label=label_names[label], alpha=0.7) plt.title('PCA on Iris Dataset') plt.xlabel('Principal Component 1') plt.ylabel('Principal Component 2') plt.legend() plt.grid(True) p...
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10

 import numpy as np  import pandas as pd  import matplotlib.pyplot as plt  import seaborn as sns  from sklearn.datasets import load_breast_cancer  from sklearn.cluster import KMeans  from sklearn.preprocessing import StandardScaler  from sklearn.decomposition import PCA  from sklearn.metrics import confusion_matrix, classification_report  # Load dataset  data = load_breast_cancer()  X, y = data.data, data.target  # Standardize features  X_scaled = StandardScaler().fit_transform(X)  # Apply KMeans clustering  kmeans = KMeans(n_clusters=2, random_state=42, n_init=10)  y_kmeans = kmeans.fit_predict(X_scaled)  # Evaluate clustering  print("Confusion Matrix:")  print(confusion_matrix(y, y_kmeans))  print("\nClassification Report:")  print(classification_report(y, y_kmeans))  # Reduce dimensions for visualization  pca = PCA(n_components=2)  X_pca = pca.fit_transform(X_s...
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9

 import numpy as np  import matplotlib.pyplot as plt  from sklearn.datasets import fetch_olivetti_faces  from sklearn.model_selection import train_test_split, cross_val_score  from sklearn.naive_bayes import GaussianNB  from sklearn.metrics import accuracy_score, classification_report,  confusion_matrix  # Load dataset  data = fetch_olivetti_faces(shuffle=True, random_state=42)  X, y = data.data, data.target  # Train-test split  X_train, X_test, y_train, y_test = train_test_split(X, y,  test_size=0.3, random_state=42)  # Train Naive Bayes model  model = GaussianNB()  model.fit(X_train, y_train)  y_pred = model.predict(X_test)  # Evaluation  print(f'Accuracy: {accuracy_score(y_test, y_pred) * 100:.2f}%')  print("\nClassification Report:")  print(classification_report(y_test, y_pred, zero_division=0))  print("\nConfusion Matrix:")  print(confusion_matrix(y_test, y_pred))...
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8

 import numpy as np  import matplotlib.pyplot as plt  from sklearn.datasets import load_breast_cancer  from sklearn.model_selection import train_test_split  from sklearn.tree import DecisionTreeClassifier, plot_tree  from sklearn.metrics import accuracy_score  # Load dataset  data = load_breast_cancer()  X, y = data.data, data.target  # Train-test split  X_train, X_test, y_train, y_test = train_test_split(X, y,  test_size=0.2, random_state=42)  # Train Decision Tree  clf = DecisionTreeClassifier(random_state=42)  clf.fit(X_train, y_train)  # Accuracy  y_pred = clf.predict(X_test)  print(f"Accuracy: {accuracy_score(y_test, y_pred) * 100:.2f}%")  # Predict one sample  sample = X_test[0]  predicted_label = clf.predict([sample])[0]  print("Predicted Class:", "Benign" if predicted_label == 1 else  "Malignant")  # Plot tree  plt.figure(figsize=(12, 8))  plot_tree(c...
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7

 import pandas as pd import matplotlib.pyplot as plt from sklearn.datasets import fetch_california_housing from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error, r2_score from sklearn.pipeline import make_pipeline from sklearn.preprocessing import PolynomialFeatures, StandardScaler def linear_regression_california():     data = fetch_california_housing(as_frame=True)     X = data.data[["AveRooms"]]     y = data.target     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)     model = LinearRegression()     model.fit(X_train, y_train)     y_pred = model.predict(X_test)     sorted_idx = X_test.values.flatten().argsort()     plt.scatter(X_test, y_test, color="blue", label="Actual")     plt.plot(X_test.values[sorted_idx], y_pred[sorted_idx], color="red", l...
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6

 import numpy as np import matplotlib.pyplot as plt def weight(x, xi, tau):     return np.exp(-np.sum((x - xi) ** 2) / (2 * tau ** 2)) def predict(x, X, y, tau):     W = np.diag([weight(x, xi, tau) for xi in X])     theta = np.linalg.pinv(X.T @ W @ X) @ X.T @ W @ y     return x @ theta # Generate training data X = np.linspace(0, 2 * np.pi, 100) y = np.sin(X) + 0.1 * np.random.randn(100) X_ = np.c_[np.ones(X.shape), X]  # Add bias term # Generate test data x_test = np.linspace(0, 2 * np.pi, 200) x_test_ = np.c_[np.ones(x_test.shape), x_test] # Predict tau = 0.5 y_pred = [predict(xi, X_, y, tau) for xi in x_test_] # Plot plt.scatter(X, y, alpha=0.6, label="Data") plt.plot(x_test, y_pred, color="red", label="LWR Prediction") plt.legend() plt.title("Locally Weighted Regression") plt.show()
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5

 import numpy as np import matplotlib.pyplot as plt from collections import Counter np.random.seed(42) # Generate data data = np.random.rand(100) train_data = data[:50] test_data = data[50:] train_labels = np.array(["Class1" if x <= 0.5 else "Class2" for x in train_data]) # k-NN function def knn(train_data, train_labels, test_point, k):     distances = np.abs(train_data - test_point)     nearest_labels = train_labels[np.argsort(distances)[:k]]     return Counter(nearest_labels).most_common(1)[0][0] # k values to test k_values = [1, 2, 3, 4, 5, 20, 30] # Loop over each k for k in k_values:     predictions = np.array([knn(train_data, train_labels, x, k) for x in test_data])          class1 = test_data[predictions == "Class1"]     class2 = test_data[predictions == "Class2"]     plt.figure(figsize=(8, 3))     plt.scatter(train_data, np.zeros_like(train_data), c="black", label="Train", mark...
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