Student's Dropout Prediction using Supervised Machine Learning Classifiers

A Data science project on Predicting Students' dropout using Machine Learning classification models

Introduction

The goal of this project is to develop a predictive model using machine learning classification algorithms to identify students who are likely to drop out. By leveraging data on student demographics, academic performance, socio-economic factors, and other relevant variables, the aim is to build a robust predictive model that can effectively forecast the likelihood of students dropping out.

The data gathered are from both internal and external sources, pulling information from different institutions. This data was drawn from various databases within universities and colleges. It covers student records spanning from the academic year 2008/2009 to 2018/2019.

This model can then be used by educational institutions to allocate resources, implement early intervention strategies, and support at-risk students.

Problem

In today's educational landscape, student retention and success are of utmost importance for educational institutions. Identifying students who are at risk of dropping out and implementing timely interventions can greatly contribute to improving graduation rates and ensuring academic success. What are certain factors that may affect students' retention or dropout in academic institutions?

Methodology Approach

The methodology adopted during the course of this project includes:

• Data collected through Kaggle datasets.
• Data processing and descriptive analysis
• Exploratory Data Analysis using Python visualization tools to gain insights into the data and identify any patterns or trends.
• Predictive analysis using Machine Learning Classification algorithms.

Dependencies

• Data Wrangling and processing Libraries
  • Pandas
  • Numpy
• Visualization Libraries
  • Matplotlib
  • Plotly express
  • Plotly Graph Objects
• Machine Learning Libraries
  • Scikit Learn

Deliverables

• Comprehensive Jupyter notebooks containing codes and results

Findings/Results

Through analysis, it is deduced that two factors are to be considered when predicting the student's retention/dropout (not limited to):

• Age at enrollment
• Course Others that showed significance are:
• Tuition fees up to date
• 1st and 2nd Semester performance
• Application mode

Out of five classification algorithms, the classifier that performed best after several tries was Decision Tree with an 83% accuracy and 0.78 AUC score

Appendix

The table below gives a brief description of each column/field in the dataset

S/N	Field	Description	Categories explained
1.	Marital status	The marital status of the student.	1—Single 2—Married 3—Widower 4—Divorced 5—Facto union 6—Legally separated
2.	Application mode	Method of application used by student	1—1st phase—general contingent 2—Ordinance No. 612/93 3—1st phase—special contingent (Azores Island) 4—Holders of other higher courses 5—Ordinance No. 854-B/99 6—International student (bachelor) 7—1st phase—special contingent (Madeira Island) 8—2nd phase—general contingent 9—3rd phase—general contingent 10—Ordinance No. 533-A/99, item b2) (Different Plan) 11—Ordinance No. 533-A/99, item b3 (Other Institution) 12—Over 23 years old 13—Transfer 14—Change in course 15—Technological specialization diploma holders 16—Change in institution/course 17—Short cycle diploma holders 18—Change in institution/course (International)
3.	Application order	The order in which the student applied
4.	Course	The course taken by the student	1—Biofuel Production Technologies 2—Animation and Multimedia Design 3—Social Service (evening attendance) 4—Agronomy 5—Communication Design 6—Veterinary Nursing 7—Informatics Engineering 8—Equiniculture 9—Management 10—Social Service 11—Tourism 12—Nursing 13—Oral Hygiene 14—Advertising and Marketing Management 15—Journalism and Communication 16—Basic Education 17—Management (evening attendance)
5.	Daytime/evening attendance	Whether the student attends classes during the day or in the evening	1—daytime 0—evening
6.	Previous qualification	The qualification obtained by the student before enrolling in higher education	1—Secondary education 2—Higher education—bachelor’s degree 3—Higher education—degree 4—Higher education—master’s degree 5—Higher education—doctorate 6—Frequency of higher education 7—12th year of schooling—not completed 8—11th year of schooling—not completed 9—Other—11th year of schooling 10—10th year of schooling 11—10th year of schooling—not completed 12—Basic education 3rd cycle (9th/10th/11th year) or equivalent 13—Basic education 2nd cycle (6th/7th/8th year) or equivalent 14—Technological specialization course 15—Higher education—degree (1st cycle) 16—Professional higher technical course 17—Higher education—master’s degree (2nd cycle)
7.	Nationality	The nationality of the student	1—Portuguese 2—German 3—Spanish 4—Italian 5—Dutch 6—English 7—Lithuanian 8—Angolan 9—Cape Verdean 10—Guinean 11—Mozambican 12—Santomean 13—Turkish 14—Brazilian 15—Romanian 16—Moldova (Republic of) 17—Mexican 18—Ukrainian 19—Russian 20—Cuban 21—Colombian
8.	Mother's qualification Father's qualification	The qualification of the student's mother and father	1—Secondary Education—12th Year of Schooling or Equivalent 2—Higher Education—bachelor’s degree 3—Higher Education—degree 4—Higher Education—master’s degree 5—Higher Education—doctorate 6—Frequency of Higher Education 7—12th Year of Schooling—not completed 8—11th Year of Schooling—not completed 9—7th Year (Old) 10—Other—11th Year of Schooling 11—2nd year complementary high school course 12—10th Year of Schooling 13—General commerce course 14—Basic Education 3rd Cycle (9th/10th/11th Year) or Equivalent 15—Complementary High School Course 16—Technical-professional course 17—Complementary High School Course—not concluded 18—7th year of schooling 19—2nd cycle of the general high school course 20—9th Year of Schooling—not completed 21—8th year of schooling 22—General Course of Administration and Commerce 23—Supplementary Accounting and Administration 24—Unknown 25—Cannot read or write 26—Can read without having a 4th year of schooling 27—Basic education 1st cycle (4th/5th year) or equivalent 28—Basic Education 2nd Cycle (6th/7th/8th Year) or equivalent 29—Technological specialization course 30—Higher education—degree (1st cycle) 31—Specialized higher studies course 32—Professional higher technical course 33—Higher Education—master’s degree (2nd cycle) 34—Higher Education—doctorate (3rd cycle)
9.	Mother's occupation Father's occupation	The occupation of the student's Mother and Father	1—Student 2—Representatives of the Legislative Power and Executive Bodies, Directors, Directors and Executive Managers 3—Specialists in Intellectual and Scientific Activities 4—Intermediate Level Technicians and Professions 5—Administrative staff 6—Personal Services, Security and Safety Workers, and Sellers 7—Farmers and Skilled Workers in Agriculture, Fisheries,and Forestry 8—Skilled Workers in Industry, Construction, and Craftsmen 9—Installation and Machine Operators and Assembly Workers 10—Unskilled Workers 11—Armed Forces Professions 12—Other Situation; 13—(blank) 14—Armed Forces Officers 15—Armed Forces Sergeants 16—Other Armed Forces personnel 17—Directors of administrative and commercial services 18—Hotel, catering, trade, and other services directors 19—Specialists in the physical sciences, mathematics, engineering,and related techniques 20—Health professionals 21—Teachers 22—Specialists in finance, accounting, administrative organization,and public and commercial relations 23—Intermediate level science and engineering techniciansand professions 24—Technicians and professionals of intermediate level of health 25—Intermediate level technicians from legal, social, sports, cultural,and similar services 26—Information and communication technology technicians 27—Office workers, secretaries in general, and data processing operators 28—Data, accounting, statistical, financial services, and registry-related operators 29—Other administrative support staff 30—Personal service workers 31—Sellers 32—Personal care workers and the like 33—Protection and security services personnel 34—Market-oriented farmers and skilled agricultural and animal production workers 35—Farmers, livestock keepers, fishermen, hunters and gatherers,and subsistence 36—Skilled construction workers and the like, except electricians 37—Skilled workers in metallurgy, metalworking, and similar 38—Skilled workers in electricity and electronics 39—Workers in food processing, woodworking, and clothing and other industries and crafts 40—Fixed plant and machine operators 41—Assembly workers 42—Vehicle drivers and mobile equipment operators 43—Unskilled workers in agriculture, animal production, and fisheries and forestry 44—Unskilled workers in extractive industry, construction,manufacturing, and transport 45—Meal preparation assistants 46—Street vendors (except food) and street service providers
10.	Displaced	Whether the student is a displaced person	1—yes 0—no
11.	Educational special needs	Whether the student has any special educational needs	1—yes 0—no
12.	Debtor	Whether the student is a debtor or not	1—yes 0—no
13.	Tuition fees up to date	Whether the student's tuition fees are up to date	1—yes 0—no
14.	Gender	The gender of the student	1—male 0—female
15.	Scholarship holder	Whether the student is a scholarship holder	1—yes 0—no
16.	Age at enrollment	The age of the student at the time of enrollment
17.	International	Whether the student is an international student	1—yes 0—no
18.	Curricular units 1st & 2nd sem (credited)	The number of curricular units credited by the student in the first and second semester
19.	Curricular units 1st & 2nd sem (enrolled)	The number of curricular units enrolled by the student in the first and second semester
20.	Curricular units 1st & 2nd sem (evaluations)	The number of curricular units evaluated by the student in the first and second semester
21.	Curricular units 1st & 2nd sem (approved)	The number of curricular units approved by the student in the first and second semester
22.	Curricular units 1st & 2nd sem (grade)	The number of curricular units grade by the student in the first and second semester
23.	Unemployment rate	The Unemployment rate %
24.	Inflation rate	The Inflation rate %
25.	GDP	GDP per capita (USD)
26.	Target	Status of the student	Graduate Dropout Enrolled