https://github.com/hafeez-urrehman/mental-health-analyzer

Mental-Health-Analyzer is an AI-Based project for predicting mental health disorders such as stress, anxiety, depression, and loneliness. By applying machine learning techniques, this project analyzes user inputs and behavioral data to provide accurate predictions, aiming to support mental well-being and early intervention.
https://github.com/hafeez-urrehman/mental-health-analyzer

data-analysis data-science early-diagnonosis machine-learning mental-health mental-wellbeing predictive-modeling python

Last synced: 1 day ago
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Mental-Health-Analyzer is an AI-Based project for predicting mental health disorders such as stress, anxiety, depression, and loneliness. By applying machine learning techniques, this project analyzes user inputs and behavioral data to provide accurate predictions, aiming to support mental well-being and early intervention.

• Host: GitHub
• URL: https://github.com/hafeez-urrehman/mental-health-analyzer
• Owner: Hafeez-UrRehman
• License: mit
• Created: 2024-08-27T18:27:26.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-09-30T15:28:31.000Z (3 months ago)
• Last Synced: 2024-11-10T17:17:59.414Z (about 2 months ago)
• Topics: data-analysis, data-science, early-diagnonosis, machine-learning, mental-health, mental-wellbeing, predictive-modeling, python
• Language: Jupyter Notebook
• Homepage:
• Size: 422 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Mental-Health-Analyzer

This project focuses on analyzing various factors that could lead to mental health disorders. It uses machine learning techniques to predict which mental health disorder an individual is likely to have based on a set of behavioral and psychological features.

## Overview

Mental health issues are a growing concern, and early detection is critical for providing timely support. This project aims to predict the possibility of mental health disorders based on various symptoms and behaviors. We analyze a dataset with multiple features related to anxiety, depression, stress and loneliness, and apply Logistic Regression to classify individuals.

## Dataset

The dataset includes several features that can predict mental and emotional states, behaviors, and physical symptoms:

- feeling.nervous: Frequency of feeling nervous

- panic: Occurrence of panic attacks

- breathing.rapidly: Instances of rapid breathing

- sweating: Frequency of sweating due to anxiety

- trouble.in.concentration: Difficulty in maintaining concentration

- having.trouble.in.sleeping: Difficulty in sleeping

- having.trouble.with.work: Trouble managing work or tasks

- hopelessness: Feeling of hopelessness

- anger: Experience of anger and frustration

- over.react: Tendency to overreact

- weight.gain: Weight fluctuations

- material.possessions: Attachment to material possessions

- introvert: Introversion tendencies

- popping.up.stressful.memory: Recurrence of stressful memories

- having.nightmares: Frequent nightmares

- avoids.people.or.activities: Avoidance of people or social activities

- feeling.negative: Consistent negative feelings

- trouble.concentrating: Difficulty concentrating

- blaming.yourself: Tendency to blame oneself

- Disorder: Target variable, indicating which mental health disorder an individual have