ag00dman/student-depression-analysis

Assignment #1: EDA & Dataset

Predicting and Preventing Student Depression

Student: Amit Goodman Program: Economics & Entrepreneurship, Reichman University (RUNI) Date: March 2026

Project Overview

In this project, I explore the "Student Depression Dataset" to build a narrative around student well-being. By analyzing academic pressure, financial stress, and lifestyle habits, I aim to identify predictable risk factors and uncover actionable protective measures.

Data Handling & Quality Protocol

To ensure a high-quality analysis, I developed a strict cleaning protocol executed directly within the project notebook. While the source CSV remains in its original state, the following steps are performed via code to prepare the data for analysis:

• —Outlier Removal: I identify and remove rows with a 0.0 CGPA, treating them as data entry errors.
• —Domain Filtering: I restrict the dataset to the Student profession to keep the focus contextually relevant to academic life.
• —Missing Value Management: I address the negligible 0.01% of missing values in Financial Stress and drop irrelevant identifiers like id.

Exploratory Data Analysis (EDA)

1. Defining the Problem

I began by analyzing the distribution of depression within the student population to understand the scope of the challenge.

[image] [image]

2. The Primary Risk Factors: Pressure & Finance

I focused on the intersection of academic and economic stress. My analysis shows a direct correlation between these stressors and mental health outcomes.

[image] [image]

Key Insight: Academic Pressure and Financial Stress emerged as the most significant predictors, showing a high density of depression cases at the upper levels of these scales.

3. The Protective Buffers: Satisfaction & Lifestyle

I looked for "Actionable Measures" - factors that can mitigate these risks.

[image] [image]

The Resolution (Conclusion)

The data reveals that the relationship between lifestyle and mental health is more complex than a simple "more is better" rule.

My Key Findings:

• —The High-Risk Extreme: Students sleeping less than 5 hours are at the absolute highest risk of depression.
• —The Sleep Paradox: Interestingly, the data indicates that students in the 7-8 hour bracket actually have a higher rate of depression than those in the 5-6 hour bracket. This suggests that in this specific population, standard sleep duration might correlate with other depressive symptoms like lethargy.
• —Satisfaction Matters: High Study Satisfaction acts as a primary protective factor, even when academic pressure is high.
• —Actionable Advice: To lower the statistical risk of depression, my findings suggest prioritizing a healthy diet, ensuring high alignment with one's academic path, and avoiding the "extreme low" of sleep (<5 hours).

Files in this Repository

• —Student_Depression_Dataset.csv: The raw, original dataset used for this project.
• —Assignment_1_EDA_&_Dataset_Amit_Goodman.ipynb: The complete Python analysis, including the data cleaning scripts, visualizations, and insights.
• —Assignment #1 - EDA & Dataset - Amit Goodman.mov: The project presentation video.