# Step 1: Import necessary libraries import pandas as pd import numpy as np from sklearn . model_selection import train_test_split from sklearn . preprocessing import LabelEncoder , StandardScaler from sklearn . svm import SVC from sklearn . tree import DecisionTreeClassifier , plot_tree from sklearn . metrics import classification_report , confusion_matrix import matplotlib . pyplot as plt import seaborn as sns # Step 2: Create a sample dataset # Features: Attention Span, Response Time, Interaction Level, Comprehension Score data = { 'attention_span' : [ 20 , 50 , 15 , 45 , 10 , 55 , 12 , 48 , 25 , 60 ], # in minutes 'response_time' : [ 5 , 3 , 8 , 2 , 10 , 2 , 9 , 3 , 6 , 2 ], # in seconds 'interaction_level' : [ 1 , 3 , 1 , 4 , 1 , 4 , 2 , 5 , 2 , 5 ], # scale of 1-5 'comprehension_score' : [ 60 , 85 , 55 , 90 , 50 , 88 , 58 , 92 , 65 , 95 ], # out of 100 ...
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