BOB

import numpy as np  import pandas as pd from scipy.spatial.distance import pdist, squareform  data = {'Attribute1': [2, 5, 1, 6], 'Attribute2': [3, 8, 2, 7]}  df = pd.DataFrame(data) print("Dataset:")  print(df) dissimilarity = pdist(df.values, metric='euclidean')  dissimilarity_matrix = squareform(dissimilarity)  dissimilarity_df = pd.DataFrame(dissimilarity_matrix, index=['Instance1','Instance2','Instance3','Instance4'],  columns=['Instance1','Instance2','Instance3','Instance4']) print("\nDissimilarity Matrix:")  print(dissimilarity_df)

import numpy as np import pandas as pd from scipy.spatial.distance import pdist, squareform data = {'Attribute1': [2, 5, 1, 6], 'Attribute2': [3, 8, 2, 7]} df = pd.DataFrame(data) print("Dataset:") print(df) dissimilarity = pdist(df.values, metric='euclidean') dissimilarity_matrix = squareform(dissimilarity) dissimilarity_df = pd.DataFrame(dissimilarity_matrix, index=['Instance1','Instance2','Instance3','Instance4'], columns=['Instance1','Instance2','Instance3','Instance4']) print("\nDissimilarity Matrix:") print(dissimilarity_df)

import numpy as np import pandas as pd from scipy.spatial.distance import pdist, squareform num_instances = int(input("Enter number of instances (rows): ")) num_attributes = int(input("Enter number of attributes (columns): ")) data = [] for i in range(num_instances):     print(f"\nEnter values for Instance {i+1}:")     row = []     for j in range(num_attributes):         value = float(input(f" Attribute {j+1}: "))         row.append(value)     data.append(row) df = pd.DataFrame(data, columns=[f'Attribute{j+1}' for j in range(num_attributes)]) print("\nDataset:") print(df) dissimilarity = pdist(df.values, metric='euclidean') dissimilarity_matrix = squareform(dissimilarity) instance_labels = [f'Instance{i+1}' for i in range(num_instances)] dissimilarity_df = pd.DataFrame(dissimilarity_matrix,     index=instance_labels,        columns=instance_labels) print("\nDissimilarity M...

import java.util.*; public class Apriori {     static List<Set<String>> dataset = Arrays.asList(             new HashSet<>(Arrays.asList("Milk", "Bread", "Eggs")),             new HashSet<>(Arrays.asList("Milk", "Bread")),             new HashSet<>(Arrays.asList("Milk", "Eggs")),             new HashSet<>(Arrays.asList("Bread", "Eggs")),             new HashSet<>(Arrays.asList("Milk", "Bread", "Butter"))     );     static double minSupport = 0.4;     public static Set<String> getAllItems(List<Set<String>> dataset) {         Set<String> items = new HashSet<>();         for (Set<String> transaction : dataset) {             items.addAll(transaction);   ...

from mlxtend.frequent_patterns import apriori, association_rules from mlxtend.preprocessing import TransactionEncoder import pandas as pd dataset = [     ['I1', 'I2', 'I3', 'I4'],     ['I1', 'I2', 'I3'],     ['I1', 'I2'],     ['I1', 'I4'],     ['I2', 'I4'] ] te = TransactionEncoder() te_ary = te.fit(dataset).transform(dataset) df = pd.DataFrame(te_ary, columns=te.columns_) print("Transaction Data:") print(df) frequent_itemsets = apriori(df, min_support=0.4, use_colnames=True) print("\nFrequent Itemsets:") print(frequent_itemsets) rules = association_rules(frequent_itemsets, metric="confidence", min_threshold=0.6) print("\nAssociation Rules:") print(rules[['antecedents', 'consequents', 'support', 'confidence', 'lift']]) Output  Transaction Data:       I1 I2 I3 I4 0 True True True True 1...

import pandas as pd from sklearn.model_selection import train_test_split from sklearn.naive_bayes import GaussianNB from sklearn.metrics import accuracy_score,confusion_matrix from sklearn.datasets import load_iris iris=load_iris() X=iris.data y=iris.target X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3) model=GaussianNB() model.fit(X_train,y_train) y_pred=model.predict(X_test) acc=accuracy_score(y_test,y_pred) cm=confusion_matrix(y_test,y_pred) print(acc) print(cm) Output  Accuracy: 0.9555555555555556 Confusion Matrix: [[17 0 0]  [ 0 17 1]  [ 0 1 9]]