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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_scaled) # Prepare DataFrame for plotting df = pd.DataFrame(X_pca, columns=['PC1', 'PC2']...
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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)) # Cross-validation cv_score = cross_val_score(model, X, y, cv=5).mean() print(f'\nCr...
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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(clf, filled=True, feature_names=data.feature_names, class_names=data.target_names) plt.ti...
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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", label="Predicted") plt.xlabel("AveRooms...
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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: plt.scatter(class1, np.ones_like(class1), c="blue", label="Class1 (Test)") if len(class2) > 0: plt.scatter(class2, np.ones_like(class2), c="red", label="Class2 (Test)") plt.title(f"k-NN Classification (k={k})") plt.yticks([]) plt.legend() plt.tight_layout() plt.show()
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4

import pandas as pd def find_s_algorithm(file_path): # Load the dataset data = pd.read_csv(file_path) print("\nTraining Data:\n", data) attributes = data.columns[:-1] # All columns except the target target = data.columns[-1] # Target column (last one) # Step 1: Initialize hypothesis with the first positive example hypothesis = None for _, row in data.iterrows(): if row[target] == 'Yes': hypothesis = list(row[attributes]) break # If no positive examples found if hypothesis is None: print("No positive examples in the dataset.") return None # Step 2: Generalize the hypothesis using other positive examples for _, row in data.iterrows(): if row[target] == 'Yes': for i in range(len(attributes)): if hypothesis[i] != row[attributes[i]]: hypothesis[i] = '?' ...
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