🧠 Mental Health in Students Post-COVID: A Silent Crisis

📊 An Exploratory Data Analysis of 16,000+ Students

📌 Project Overview

The COVID-19 pandemic disrupted education systems worldwide, but one of the most silent crises has been the toll on students’ mental health. This project leverages real survey data (16k+ students) to explore depression, suicidal thoughts, and lifestyle/academic stressors affecting students post-COVID.

🎯 Business & Research Objective

• Identify at-risk student groups based on demographics, academic pressure, and lifestyle factors.
• Quantify the impact of financial stress, sleep, diet, and study satisfaction on depression & suicidal thoughts.
• Translate raw survey data into actionable insights that educators, policymakers, and mental health professionals can use.

🗂️ Project Structure

datasets/
    ├── post_covid_mental_health_cleaned.xlsx   # Cleaned dataset
notebooks/
    └── Mental_Health_in_Students_Post_COVID_EDA.ipynb
reports/
    └── EDA_Report.pdf   # Full PDF Storytelling Report

🧼 Data Preprocessing

• Encoding: Binary mappings (Yes/No → 1/0), ordinal scaling (Healthy=2 → Unhealthy=0).
• Cleaning: Merged duplicates (e.g., Class 12 inconsistencies), dropped irrelevant/missing entries.
• Feature Engineering: Academic pressure (1–5 scale), financial stress levels, lifestyle scores.

🔍 Exploratory Data Analysis

Key EDA activities included:

• Demographics: Gender & age-based risk patterns.
• Academic Pressure: High stress vs depression & suicidal thoughts.
• Degree Programs: “Prestige” degrees (MBBS, B.Tech) showing 65–70% depression rates.
• Lifestyle Factors: Sleep & diet vs mental health.
• External Factors: Financial stress & family history of illness.

📌 Insights & Observations

• Students aged 21 had the highest number of depression cases.
• Depression was significantly higher among students with high academic pressure and low study satisfaction.
• Some degrees (e.g., B.Tech, MBBS) showed over 50%+ depression rates.
• Students getting < 6 hours of sleep and unhealthy diets were more likely to report suicidal thoughts.

Example Visuals (from EDA)

Financial Stress vs Depression

Fee Hike Highlights

💡 Insights & Observations

• Age Risk: Students aged ~20–21 most affected (transition to adulthood + academic pressure).
• Prestige Pressure: Mechanical Engg. (70%), MBBS (67%), B.Tech (65%) show extreme depression levels.
• Financial Stress Multiplier:
  • Low academic + low financial stress → 8.8% depression
  • High academic + high financial stress → 96% depression
• Sleep & Diet ≠ Protection: Even healthy lifestyles don’t shield students when stress is systemic.
• Study Satisfaction Buffer: With high academic pressure, satisfaction reduced depression from 93% → 83%.

🛠️ Tech Stack

• Python (Pandas, Seaborn, Matplotlib, NumPy)
• Jupyter Notebook (analysis + visual storytelling)
• Excel (initial data cleaning, exploration)

📌 Deliverables

• ✅ Cleaned dataset → post_covid_mental_health_cleaned.xlsx
• ✅ Full Jupyter Notebook EDA → Mental_Health_in_Students_Post_COVID_EDA.ipynb
• ✅ PDF Report (storytelling deck) → reports/EDA_Report.pdf

📢 Impact & Takeaways

• Depression isn’t just personal — it’s systemic (academic + financial + societal).

• Grades & hours studied matter less than stress, satisfaction, and financial security.

• Policy & education models must shift focus from “perfect students” → mentally healthy humans.

🔗 Connect

• 🔗 LinkedIn - Danish Shaikh
• 📊 Kaggle Profile
• 💻 GitHub Profile

🏆 Final Note

This isn’t just an analysis. It’s a data-driven story that uncovers how systemic pressures are breaking students silently.

“We don’t need more perfect students. We need happier, healthier, heard humans.”