This project focuses on analyzing the relationship between mental health and technology usage. It includes data extraction, cleaning, visualization, and a web application using Streamlit. The goal is to understand how different aspects of technology usage, such as social media and gaming, impact mental health indicators like stress levels and sleep patterns.
The objective of this project is to perform a comprehensive analysis of a dataset related to mental health and technology usage, including extraction, cleaning, visualization, and the creation of an interactive web application using Streamlit.
This project allows:
- 📥 Extracting and cleaning data.
- 📊 Visualizing data through interactive charts.
- 🌐 Deploying an interactive web application to visualize the results.
- 🐍 Python: Main programming language.
- 📊 Pandas: Data manipulation and analysis.
- 📈 Seaborn and Matplotlib: Data visualization.
- 🌐 Streamlit: Creating interactive web applications.
- 📓 Jupyter Notebooks: Development and documentation of the process.
- 📥 Data Extraction: Obtaining data from Kaggle.
- 🧹 Data Cleaning: Transforming and cleaning the data to ensure its quality.
- 📊 Data Visualization: Creating charts to explore and understand the data.
- 🌐 Web Application Deployment: Creating an interactive web application to visualize the results.
The results of the analysis include:
- 📊 Interactive charts showing the distribution of different variables.
- 🌐 An interactive web application to explore the results, click here
The conclusions of the project highlight the importance of data cleaning and visualization to understand patterns and trends in the data. The analysis provides insights into how technology usage affects mental health indicators.
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Access to Support Systems:
- People with better access to support systems tend to engage in more physical activity and report better mental health status. People with better access to support systems, such as friends, family, or community services, tend to participate in more physical activities. This may be because social support provides motivation and opportunities to be more active. Additionally, these individuals also report better mental health, possibly because social support can reduce stress and provide a sense of belonging and security.
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Impact of Work Environment:
- A negative work environment may be associated with fewer hours of sleep. A negative work environment, characterized by high levels of stress, poor relationships with colleagues or bosses, and unsatisfactory working conditions, may be associated with fewer hours of sleep. The stress and anxiety generated by a poor work environment can make it difficult to relax and sleep well.
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Use of Online Support:
- People who spend more time using online support tend to engage in less physical activity. People who spend more time using online support, such as forums, social networks, or chat services, tend to participate in less physical activity. This may be because the time spent on these online activities reduces the time available for physical exercise. Additionally, excessive use of electronic devices can lead to a more sedentary lifestyle.
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Technology and Social Media Usage:
- There is no strong relationship between technology usage and social media usage. There is no strong relationship between technology usage and social media usage. This suggests that the time people spend using technology (such as computers, tablets, and smartphones) is not necessarily related to the time they spend on social media. People may use technology for a variety of activities that do not include social media, such as working, studying, or entertaining themselves.
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Gender and Technology Usage:
- Technology usage is similar across different genders in this dataset. Technology usage is similar across different genders in this dataset. This indicates that there are no significant differences in the amount of time or the way men and women use technology. Both genders appear to have comparable technology usage patterns.
The web application includes the following visualizations:
- 📊 Age group distribution.
- 📊 Gender distribution.
- 📊 Technology usage hours by gender.
- 📊 Social media usage hours by gender.
- 📊 Stress level by gender.
- 📊 Gaming hours by gender.
- 📊 Sleep hours distribution by range.
📁 data/: Folder for data.📁 data.csv/: Data used for the project
📁 notebooks/: Jupyter Notebooks for each stage of the project.📓 data_cleaning.ipynb: Notebook for data cleaning.📓 data_visualization.ipynb: Notebook for data visualization.
📁 streamlit_app/: Folder for the Streamlit application.🌐 app.py: Main script for the Streamlit application.
📁 reports/: Folder for reports and visualization figures.📁 figures/: Folder for visualization figures.
📄 requirements.txt: File listing the project dependencies.📄 README.md: Project description, setup instructions, and usage.
This project is licensed under the MIT License. See the LICENSE file for more details.
For any inquiries or suggestions, please contact:
- 👤 Name: Juan Duran Bon
- 📧 Email: [email protected]
- 🐙 LinkedIn: www.linkedin.com/in/juan-duran-bon
Thank you for your interest in this project! 🙌
🌟 Thank you for exploring the Mental Health Project! 🌟
We hope this project inspires you to delve deeper into the fascinating intersection of technology and mental health. Remember, data is not just numbers; it's a powerful tool to understand and improve our lives. Keep exploring, keep questioning, and most importantly, keep caring. Together, we can make a difference. 💖
Happy coding! 🚀