Project Summary
This research project aims to raise awareness about the presence and development of student depression during the university years, as well as its negative effects on emotional well-being, academic performance, and interpersonal relationships.
Our approach is based on the use of programming and data analysis tools, such as Python and specialized libraries for data visualization, in order to detect patterns and significant relationships between variables such as age, gender, academic workload, and sleep habits.
View MethodologyResearch Team
Irving Nair Ferrusca Jaimez
Principal Investigator
Andrea Michel Portillo Rosales
Data Analyst
Jessica Camargo Olvera
Visualization Specialist
Our team collaborating on data analysis
Discussion of findings and strategies
Related Courses
Student Depression Dataset
The dataset used contains detailed information about students and various factors that may influence their mental health. Below we present an analysis of the provided dataset:
Total Records
Depression Prevalence
Average Age
Insufficient Sleep
Key Variables Analyzed
Gender distribution of participants
Age range of students analyzed
Perceived stress level from academic workload (scale 1-5)
Average hours of sleep per night
Quality of diet (Healthy, Moderate, Unhealthy)
Level of concern about economic situation (scale 1-5)
Depression diagnosis (1 = Yes, 0 = No)
Data Sample
| ID | Gender | Age | Academic Pressure | Sleep Duration | Depression |
|---|---|---|---|---|---|
| 2 | Male | 33.0 | 5.0 | 5-6 hours | Yes |
| 8 | Female | 24.0 | 2.0 | 5-6 hours | No |
| 26 | Male | 31.0 | 3.0 | Less than 5 hours | No |
| 30 | Female | 28.0 | 3.0 | 7-8 hours | Yes |
| 32 | Female | 25.0 | 4.0 | 5-6 hours | No |
| 59 | Male | 28.0 | 3.0 | 7-8 hours | Yes |
| 83 | Male | 24.0 | 3.0 | 5-6 hours | Yes |
| 103 | Female | 19.0 | 5.0 | Less than 5 hours | Yes |
| 145 | Male | 25.0 | 3.0 | 5-6 hours | Yes |
| 173 | Male | 18.0 | 4.0 | More than 8 hours | Yes |
Key Dataset Findings
- Students with less than 6 hours of sleep are 3.2 times more likely to show symptoms of depression.
- Academic pressure is the factor most correlated with depression, with a coefficient of 0.68.
- 42% of students with family history of mental illness develop depression.
- Graduate students show significantly higher levels of academic stress than undergraduate students.
- The combination of high academic pressure and insufficient sleep increases the risk of depression by 78%.
Problem Statement
In recent years, student dropout has become an extremely alarming phenomenon that not only significantly affects students emotionally, but also impacts their academic performance throughout their studies.
This is a problem that can easily go unnoticed due to how it presents itself. It tends to affect not only high school students but also higher education students, such as universities, where they are forced to face difficult decisions ranging from losing motivation to continue their studies to abandoning their programs or engaging in risky behaviors.
Analyzed Factors
- Age and gender
- Academic pressure
- Work pressure
- Overall GPA
- Satisfaction with studies
- Sleep duration
- Eating habits
Key Objectives
- Identify risk groups
- Analyze influencing factors
- Propose intervention strategies
- Recommend preventive measures
- Improve psychological support
Methodology
Problem Definition
Depression in university students is a growing mental health problem that significantly affects their academic performance, interpersonal relationships, and general well-being.
Data Collection
We used the "Student Depression Dataset" published by Adil Shamim on Kaggle, which contains relevant information about various factors affecting students.
Cleaning and Organization
We processed the data using Python (pandas) to remove null or outlier values, convert data types, and normalize variable names.
Statistical Analysis
We implemented algorithms to calculate statistical measures (mean, median, mode, standard deviation) and detect relationships between variables.
Data Visualization
We generated intuitive visualizations using Matplotlib and Seaborn to effectively communicate the findings.
Process of statistical data analysis
Creating visualizations for findings
Project Timeline
Define topic and outline the problem
Establish the project focus and properly outline the problem to be addressed.
Search and selection of dataset
Selection of data on which the project will be developed.
Literature review and theoretical framework
Research on the theoretical foundations supporting the work.
Data cleaning and organization
Essential process to ensure analysis quality.
Exploratory Data Analysis (EDA)
Identification of patterns, outliers, correlations, etc.
Data visualization
Clear presentation of findings through charts.
Final review and project delivery
Final stage where a final review is performed and the project is formally delivered.
Findings and Recommendations
Identified Risk Factors
- Academic pressure: Exams, deadlines, and thesis are significant factors.
- Lack of sleep: Students with less than 6 hours of sleep show greater propensity.
- Family expectations: High expectations correlate with higher stress levels.
- Economic problems: Financial difficulties increase risk.
Academic pressure is one of the main factors
Lack of sleep affects performance and mental health
Prevention Strategies
For Institutions
- Accessible psychological support programs
- Stress management workshops
- More flexible academic policies
- Early detection of at-risk students
For Students
- Sleep 7-8 hours daily
- Establish study and rest routines
- Talk to someone you trust
- Limit social media use
- Practice moderate exercise
Resources and Help
If you or someone you know is dealing with depression, seek help from:
- People close to you (friends, family, professors)
- Psychological counseling services at your university
- Mental health centers
- Helplines
Peer support is fundamental
Professionals can provide the necessary help