Projects And Technical Papers

Projects And Technical Papers#

Projects#

Heart Disease Prediction Model#

  • Objective: Develop and deploy a machine learning model to predict heart disease based on indicators

  • Technologies:

    • Frontend: Streamlit

    • Backend: FastAPI and Docker

    • Experiment Logs: MLFlow on DagsHub

    • Cloud Services: Digital Ocean

  • Key Features:

    • Normalized database creation and data exploration

    • Preprocessing pipelines and multiple classification algorithms with feature engineering, selection, and dimensionality reduction (PCA)

    • Model deployment with FastAPI, Docker, and Streamlit

    • Comprehensive documentation and project presentation via Jupyter Book

  • Link:

Restaurant Database#

  • Objective: Full-stack restaurant website with user authentication and reservation system with dynamic menu

  • Technologies:

    • Frontend: HTML5, CSS3, JavaScript

    • Backend: Node.js, Express.js

    • Database: PostgreSQL

    • Authentication: JWT, bcrypt

    • Cloud Services: Render, Koyeb

  • Key Features:

    • User authentication system with secure login/signup

    • Dynamic menu management with database integration

    • Real-time reservation system with user tracking

    • RESTful API endpoints for data management

  • Link:

Crime Detection using Sentiment Analysis#

  • Objective: Develop an innovative crime prediction model integrating sentiment analysis from social media with historical crime data

  • Technologies:

    • Data Collection: Playwright, PRAW

    • Data Processing: Pandas, NLTK, TextBlob

    • Machine Learning: Scikit-learn (PCA, Random Forest)

    • Data Visualization: Matplotlib

  • Key Features:

    • Automated extraction of crime-related social media data from Twitter and Reddit

    • Custom sentiment scoring mechanism with crime severity weighting

    • Integration of real-time sentiment analysis with historical crime data

    • Principal Component Analysis (PCA) for feature selection and dimensionality reduction

    • Random Forest classification for crime trend prediction

    • Geolocation-based crime severity mapping at the state level

    • Comparative analysis of predicted crime severity rankings against established crime indexes

  • Link:

Generative AI for Mental Health Support#

  • Objective: A generative AI-powered web application to provide therapeutic suggestions, mindfulness exercises, and empathetic responses for mental health support.

  • Technologies:

    • Frontend: React.js, HTML5, CSS3

    • Backend: Flask, FastAPI

    • AI Models: OpenAI GPT-4, Hugging Face Transformers

    • Database: MongoDB

    • Cloud Services: AWS, Render

    • APIs: Twilio (optional for voice/text), OpenAI API

  • Key Features:

    • Emotion-based suggestions for therapeutic exercises and motivational messages

    • User-customizable tone and response preferences

    • Privacy-focused design with no data storage

    • Safeguards to redirect users to professional resources when necessary

    • Multimodal support, including text and optional audio-guided exercises

NarrativeQA Reading Comprehension#

  • Objective: Produce natural language results from summaries and questions

  • Technologies: BiDAF attention model, GRU, LSTM, RNN

  • Key Features:

    • Natural language processing

    • Advanced attention mechanisms

Machine Learning for Indoor Navigation#

  • Objective: Assist vision-impaired individuals with indoor navigation

  • Technologies: Reinforcement Learning, DeepMind lab, Q-learning algorithm

  • Key Features:

    • Simulated agent navigation

    • Reward and penalty calibration

Technical Papers#