Mental-health-analysis

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Mental-health-analysis

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Exploring Mental Health Trends Through Data I visualized this data through: Histograms Pie charts Box plots Line graphs Scatter plots I'd love to hear your thoughts or suggestions from the community! #DataScience #Mental Health #Python #Seaborn #DataVisualization 20 19- #PublicHealth #LinkedInLearning #Analytics

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#mental health analysis
Exploring Mental Health Trends Through Datal

I recently completed a data analysis project using a real-world mental health dataset collected during the COVID-19 pandemic in the U.S.

Using Python, pandas, seaborn, and matplotlib, I explored trends in:

Medication and therapy usage

Mental health needs across gender and age

Impact of family history on treatment

17 Service usage changes over time

I visualized this data through:

Histograms

Pie charts

Box plots

Line graphs

Scatter plots

One of my key takeaways: young adults and those with a family history of mental illness were more likely to seek treatment, while disparities in access still exist-especially for marginalized groups.

This project helped me sharpen my data visualization skills and better understand how analytics can support mental health research and policymaking.

Check out a few of the charts below!

I'd love to hear your thoughts or suggestions from the community!

#DataScience

#Mental Health

#Python

#Seaborn

#DataVisualization

20

19-

#PublicHealth

#LinkedInLearning

#Analytics

# Mental Health Data Analysis

This project analyzes trends in mental health treatment across different demographics in the United States, with a focus on the use of prescription medication for mental health over time. The analysis is performed using Python libraries such as Pandas, Matplotlib, and Seaborn.

## 📁 Files

- `CA2 notebook.ipynb`: The main Jupyter notebook containing the data analysis and visualizations.
- `Python ca2 report.docx`: A written report summarizing the findings and interpretations.
- `Mental.csv`: The dataset used (you'll need to upload this separately if it's not included).

## 📊 Key Analysis

- Line plot of medication usage trends over time.
- Bar chart showing average usage by age group.
- Comparative visualization of mental health medication usage across different phases.

## 📦 Dependencies

To run the notebook, you'll need the following Python libraries:

```bash
pip install numpy pandas matplotlib seaborn

Mental-health-analysis

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