Tree Decission

Dataset Description The dataset used in this practical example, tree.csv, was developed specifically to demonstrate the application of a Decision Tree Classifier to a binary classification problem. The dataset represents student academic and behavioral characteristics and is designed to illustrate how a Decision Tree can use several explanatory variables to predict whether a student is likely to pass or fail. The dataset contains 197 observations and 6 variables. Five variables are used as input or predictor variables, while the sixth variable, Pass, represents the target variable. Each observation corresponds to a hypothetical student and contains information describing the student's study behavior, attendance, assignment completion, previous academic performance, and sleep duration. Variable Type Description Study_Hours Numerical Number of hours the student spends studying. Attendance_Percent Numerical Percentage of classes attended by the student. Assignments_Completed Numerical Number of assignments completed by the student. Previous_Score Numerical Student's previous academic score, measured on a 0–100 scale. Sleep_Hours Numerical Average number of hours the student sleeps per day. Pass Binary Target variable indicating whether the student passes: 0 = Fail, 1 = Pass.