Machine learning model used to to find the mental fitness of an individual. Mental Fitness Tracker: Machine Learning Model - README

This repository contains a machine learning model for a Mental Fitness Tracker, designed to monitor and enhance mental well-being. The model utilizes various inputs, such as self-reported mood, behavior patterns, physiological data, and social media activity, to provide personalized insights and recommendations.

Contents Data Collection: Gather a diverse dataset encompassing mental health information, including mood levels, daily activities, exercise routines, sleep patterns, and other relevant data. Incorporate data from wearable devices or smartphone sensors for additional insights.

Feature Extraction: Preprocess and extract meaningful features from the collected data. Examples include daily average mood, weekly sleep patterns, and activity levels during specific times. Apply sentiment analysis techniques for text data analysis.

Labeling: Assign labels or ratings to the collected data based on mental health indicators or known outcomes. Expert input or self-assessment questionnaires may be necessary for accurate labeling.

Model Training: Utilize machine learning techniques, such as regression, classification, or time-series analysis, to train the model on the labeled dataset. Explore deep learning or ensemble models for improved accuracy.

Model Evaluation: Assess the model's performance using appropriate evaluation metrics. Split the dataset into training and testing subsets for validation. Refine the model architecture, feature selection, or hyperparameters as needed.

Deployment and Feedback Loop: Integrate the trained model into the Mental Fitness Tracker application. Allow users to input data and receive personalized insights and recommendations. Continuously collect user feedback for ongoing model improvement.

Note: Mental well-being is a complex subject, and this tool should be used as a supplement to professional care. Ensure ethical considerations, privacy, and limitations of the model are addressed. Involve mental health professionals throughout the development process.

Please refer to the detailed documentation within the repository for instructions on running the model and deploying the Mental Fitness Tracker application.