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Mental Health Predictor: AI-ML Solution for Mental Wellness

Overview:
Developed an AI-powered mental health prediction system leveraging a boosting algorithm (ADABOOST) to assess users' mental health status based on input data. This project integrated real-time data collection, machine learning, and a chatbot for user interaction to provide tailored recommendations and insights.

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Key Contributions:

  1. Data Collection and Preprocessing:

    • Designed and implemented a real-time data collection pipeline for mental health assessments.

    • Preprocessed input data by encoding string responses into binary values for model compatibility.

  2. Machine Learning Model Development:

    • Built and trained an ADABOOST classifier using Python to predict mental health outcomes with high accuracy.

    • Performed feature engineering and model evaluation to ensure reliability across diverse user inputs.

  3. Web Application Integration:

    • Utilized Flask to seamlessly connect the front-end HTML interface with the Python back-end.

    • Created an interactive web application for users to input data and receive instant predictions and recommendations.

  4. Chatbot Development:

    • Integrated a conversational bot to provide users with personalized mental health resources and solutions based on their responses.

  5. Evaluation and Insights:

    • Evaluated the model's performance on real-time datasets, ensuring the prediction accuracy and user engagement.

    • Provided actionable insights for users to address mental health challenges effectively.

 

Skills and Technologies:

  • Programming: Python, HTML, Flask

  • Machine Learning: ADABOOST, feature engineering, model evaluation

  • Data Processing: Real-time data collection, encoding, and preprocessing

  • Web Development: Flask framework, front-end and back-end integration

  • Visualization: Interactive dashboards for user-friendly insights

 

Impact:
This project demonstrates the application of machine learning and web technologies to address critical societal challenges. It showcases expertise in predictive modeling, user-centric application design, and the integration of AI-driven solutions, aligning well with data analytics, data science, and machine learning roles.

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