The lack of an effective and comprehensive system for monitoring and improving mental fitness poses a significant challenge. Existing methods are often subjective and lack data-driven insights. To address this, we aim to develop a machine learning-based Mental Fitness Tracker that leverages data analysis and predictive models to provide personalized recommendations, enabling individuals to track and enhance their mental well-being efficiently.
The Mental Fitness Tracker is a machine learning-based system that monitors and improves mental fitness. It utilizes data analysis and predictive models to provide personalized recommendations for enhancing mental well-being. By tracking various indicators and analyzing user data, the system aims to offer actionable insights and strategies for individuals to proactively manage their mental health.
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Google Colab: Google Colab is a cloud-based Jupyter Notebook environment that allows you to write and execute Python code online. It provides a free GPU and supports popular libraries, making it ideal for machine learning projects.
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pandas: pandas is a powerful data manipulation library in Python. It provides data structures and functions to efficiently work with structured data, such as importing and exporting datasets, data cleaning, transformation, and analysis.
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matplotlib: matplotlib is a data visualization library in Python. It offers a wide range of plotting functions to create various types of charts, graphs, and plots. It is often used to visualize data and model outputs in a clear and visually appealing manner.
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scikit-learn: scikit-learn is a popular machine-learning library in Python. It provides a comprehensive set of tools and algorithms for tasks like classification, regression, clustering, and dimensionality reduction. It also includes utilities for data preprocessing, model evaluation, and hyperparameter tuning.
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Linear Regression and Random Forest Algorithm: Linear regression and random forest algorithm is a simple yet powerful machine learning algorithm used for predicting numerical values. It assumes a linear relationship between the input features and the target variable and aims to find the best-fit line that minimizes the prediction errors.
To get started with the Mental Fitness Tracker project, follow these steps:
- Clone this GitHub repository to your local machine.
- Install the required Python libraries and dependencies listed in the project's environment setup file.
- Launch Google Colab and open the provided Jupyter Notebook for this project.
- Follow the instructions within the notebook to import and analyze data, train machine learning models, and generate personalized mental fitness recommendations.
This section will provide detailed instructions on how to use the Mental Fitness Tracker. Include examples, screenshots, or code snippets to help users understand how to interact with the system effectively.
If you'd like to contribute to the project, please follow these guidelines:
- Fork the repository.
- Create a new branch for your feature or bug fix.
- Make your changes and test thoroughly.
- Submit a pull request with a clear description of your changes.
https://github.com/Roshk01/Mental_health_report_prediction_model-/blob/main/SECURITY.md
For questions or feedback, please contact the project maintainers:
- IBM SkillBuild Edunet.