Student Adaptability Prediction in Online Education by Maria Saif Student Adaptability Prediction in Online Education by Maria Saif

Student Adaptability Prediction in Online Education

Maria Saif

Data Scientist

Matplotlib

scikit-learn

seaborn

Education

ML-random-forest-algorithm-

Student Adaptability Level Prediction (Online Education)

This project focuses on predicting the adaptability level of students in an online education environment using machine learning techniques. The objective is to classify students into one of three adaptability levels — Low, Moderate, or High — based on demographic, environmental, and technological factors.

Dataset

File: students_adaptability_level_online_education.csv

Records: 1205 rows

Features: 14 columns

Input Features:

Gender

Age

Education Level

Institution Type

IT Student

Location

Load-shedding

Financial Condition

Internet Type

Network Type

Class Duration

Self LMS

Device

Target Variable:

Adaptivity Level: Low, Moderate, High

Technologies Used

Python

pandas, numpy, matplotlib, seaborn (Data analysis & visualization)

scikit-learn (Machine learning & preprocessing)

joblib (Model serialization)

Preprocessing Steps

Encoding:

Ordinal Encoding: Age, Education Level, Class Duration

One-Hot Encoding: All other categorical features

Scaling:

StandardScaler applied to numerical data

Data Split:

80% Training, 20% Testing

Machine Learning Models

Several classification algorithms were tested:

Model Accuracy Logistic Regression 72.61% K-Nearest Neighbors 76.76% Support Vector Machine 82.16% Decision Tree 86.72% Random Forest 89.21% Gradient Boosting 83.40%

Hyperparameter Tuning

A GridSearchCV was performed on the Random Forest model to find the best hyperparameters.

Best Parameters:

{
  'bootstrap': False,
  'max_depth': None,
  'min_samples_leaf': 1,
  'min_samples_split': 2,
  'n_estimators': 300
}

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Posted Jul 21, 2025

Predicted student adaptability in online education using machine learning.

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