This interactive Excel dashboard provides a comprehensive analysis of how social media usage impacts addiction, mental health, sleep patterns, and relationship conflicts among students from various countries. Using slicers, charts, and KPI summaries, this visualization enables users to explore multi-dimensional data insights intuitively.
- Source: Kaggle - Social Media Addiction vs Relationships
- Total Students Surveyed: 705
- Attributes Analyzed:
- Demographics: Country, Gender, Age, Academic Level
- Behavioral: Sleep Duration, Platform Usage, Daily Usage
- Mental Health Score & Status
- Social Media Addiction Score & Level
- Relationship Status & Conflict Frequency
Before analysis, the dataset was thoroughly cleaned using Power Query in Excel. Major transformations included:
- Renaming columns for clarity
- Removing duplicates
- Handling missing values (filtering or imputing)
- Standardizing text entries (e.g., consistent case for categories)
- Converting data types appropriately
- Creating derived columns like addiction flags, sleep categories, etc.
Power Query ensures that the dashboard remains dynamic and refreshable if the source data changes.
| Metric | Value | Insight |
|---|---|---|
| Total Students | 705 | Size of surveyed population |
| Average Mental Health | 6.23 | Indicates moderately low mental well-being |
| Highly Addicted | 77.16% | Majority of students are in the high addiction category |
| Poor Sleep | 30.78% | Nearly 1/3rd of students have poor sleep quality |
| Conflict Behavior | 63.83% | 2 out of 3 students experience conflicts related to social media |
- Objective: Compare addiction levels across genders.
- Insight: Both males and females show similar distributions, with low and moderate addiction dominating, but notable presence of high addiction as well.
- Objective: Correlate usage frequency with mental health.
- Insight:
- High usage → Lower mental health (5.85)
- Low usage → Higher mental health (8.50)
- Shows negative correlation between screen time and mental well-being.
- Objective: Analyze how relationship status affects social conflicts.
- Insight:
- Single students (384) face the most conflict.
- In relationships (289) also show high conflict.
- Complicated status (32) face the least conflicts — possibly due to social withdrawal.
- X-Axis: Top 10 countries
- Bar: % of highly addicted students
- Line: Average conflict frequency
- Insight (Top 5):
- India (16.77%) has highest addiction percentage with moderate conflict (3.53).
- USA shows high conflict score (3.80) with lower addiction.
- France & Switzerland show low addiction and low conflict.
- Objective: Show platform preference among students.
- Insight:
- Instagram (249) is the most used platform.
- Followed by TikTok (154), Facebook (123), WhatsApp (54), and Twitter (30).
- Sleep Categories: Excellent, Good, Poor, Very Poor
- Gender: Male / Female
- Enable customized insights into subgroups (e.g., “Female students with poor sleep”).
🧍♂️ The dataset shows that the majority of students are highly addicted to social media. Despite representing a global sample, commonalities appear across countries and genders.
🧠 With increasing screen time, mental health scores drop significantly, highlighting the emotional toll of digital engagement.
💬 Most students, especially those who are single or heavily online, report high conflict frequency, suggesting that digital behavior spills into social stress.
📉 Platforms like Instagram and TikTok dominate, raising concerns over the dopamine feedback loops and platform design influencing student behavior.
🛌 Finally, sleep disruption (30.78%) emerges as a critical health indicator, reinforcing that digital overuse affects not just the mind but also physical recovery.
| Tool/Feature | Purpose |
|---|---|
| Power Query | Cleaning, transformation, derived columns |
| PivotTables | Data aggregation |
| Charts | Combo, stacked bar, column, line |
| Slicers | Interactive filtering |
| Conditional Formatting | Visual emphasis |
| COUNTIF, AVERAGEIF | KPI calculations |
- Digital Wellness Programs should be prioritized in educational institutions.
- Students should be educated about screen time management.
- Social support and mental health counseling can be targeted toward high-risk groups.
- Encourage platform usage awareness through workshops.
- Future studies should explore longitudinal effects of these digital behaviors.
social media vs studens.png: Dashboard ScreenshotREADME.md: This documentationStudents-Social-Media-Addiction-Excel-project.xlsx: Excel file with Power Query & Dashboard (not included here)- Dataset: Kaggle CSV file