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", marker="x") if len(class1) > 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()

Comments

Post a Comment

Popular posts from this blog

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']...
Read more

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) pl...
Read more

1

import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from sklearn.datasets import fetch_california_housing # Load California housing dataset housing_data = fetch_california_housing() df = pd.DataFrame(housing_data.data, columns=housing_data.feature_names) # 1. Display First Five Rows print("First five rows of the dataset:") print(df.head()) # 2. Dataset Summary print("\nDataset Summary:") print(df.describe()) # 3. Histograms for All Features df.hist(figsize=(12, 8), bins=30, edgecolor='black') plt.suptitle("Histograms for All Numerical Features", fontsize=16) plt.show() # 4. Boxplots for All Features plt.figure(figsize=(12, 6)) df.boxplot(rot=45) plt.title("Box Plots for All Numerical Features", fontsize=16) plt.show() # 5. Outlier Detection using IQR Q1 = df.quantile(0.25) Q3 = df.quantile(0.75) IQR = Q3 - Q1 lower_bound = Q1 - 1.5 * IQR upper_bound = Q3 + 1.5 * IQR ou...
Read more