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Here, you’ll find a collection of my previous projects, each showcasing my expertise in transforming complex challenges into actionable outcomes. I invite you to explore these projects and how I can apply the same expertise to your ideas. Take a look around, and let's explore how we can create something remarkable together!

PCOD Risk Prediction Model

Objective: To develop a comprehensive machine learning-based tool that predicts the likelihood of Polycystic Ovarian Disease (PCOD) based on patient-provided demographic, lifestyle, symptom, and medical data, while also offering personalized guidance for the next steps.

Key Features and Functionalities:

  1. PCOD Risk Prediction:

    • Built a robust machine learning model using Gradient Boosting Machines (GBM) to predict the likelihood of PCOD based on users' demographic details, daily symptoms, and routine health check-up data.

    • The model provides an accurate assessment of PCOD risk for an individual.

  2. Personalized Dietary and Lifestyle Recommendations:

    • For individuals identified at an early or manageable stage, the tool automatically generates a customized dietary and exercise plan, helping them manage and potentially reverse the condition through lifestyle modifications.

  3. Doctor and Hospital Recommendations:

    • If the assessed risk indicates the need for medical intervention, the tool recommends the best hospitals and specialists nearby based on the user's geographical location, helping streamline access to appropriate healthcare

Data and Methodology:

  • Utilized a large-scale dataset with 700,000+ records, covering women of diverse backgrounds, ages, regions, and professions.

  • Incorporated various health parameters, including lifestyle habits, physiological symptoms, and periodic medical check-up data.

  • Applied advanced machine learning techniques with rigorous validation to ensure accuracy and reliability.

Skin Disease Prediction System

Objective:

To develop an image-based diagnostic tool that predicts various skin diseases from user-uploaded images using deep learning techniques.

Key Features and Functionalities:

  1. Image-Based Disease Prediction:

    • Designed and implemented a Convolutional Neural Network (CNN) model capable of accurately identifying different skin diseases from images.

    • Enabled real-time diagnostic assistance through user-friendly image uploads.

  2. Enhanced Model Performance:

    • Applied data augmentation techniques to expand the diversity of training data, significantly improving the model's robustness and prediction accuracy.

  3. Scalable and Accessible Tool:

    • Developed to assist both individuals and healthcare professionals in obtaining quick preliminary assessments, reducing time to diagnosis.

Course Recommendation System

Objective: To build a personalized course recommendation system for an online learning platform using machine learning and natural language processing.

Key Features and Functionalities:

  1. AI-Driven Course Recommendations:

    • Developed a recommendation engine powered by the BERT (Bidirectional Encoder Representations from Transformers) model to understand user preferences and suggest relevant courses.

  2. Priority-Based Ranking:

    • The system ranks recommendations by 1st, 2nd, and 3rd priority based on factors such as:

    • User’s previous searches.

    • Registered courses.

    • Progress and completion status of prior courses.

  3. Personalized Learning Path:

    • Tailored recommendations to align with each user’s learning journey, interests, and career goals, thereby enhancing engagement and motivation.

Outcome and Impact:

  • Enhanced the learning experience by delivering highly personalized course recommendations, resulting in improved discoverability and increased course completion rates.

  • Enhanced user satisfaction by providing relevant, goal-aligned course suggestions, contributing to better learning outcomes.

Clinical Notes Summarizer using Transformers

Objective: To automate the summarization of unstructured clinical notes into concise, structured summaries using transformer-based NLP models, thereby reducing physician workload and improving clinical decision support.

Key Features and Functionalities:

  • Automated Summarization:
    Leveraged a fine-tuned BERT-based model to generate short, context-aware summaries of clinical text from electronic health records (EHR).

  • Medical Entity Extraction:
    Integrated named entity recognition (NER) to highlight key components like symptoms, medications, diagnoses, and procedures.

  • Real-Time API Deployment:
    Deployed the model via FastAPI to enable real-time summarization within existing hospital systems.

Data and Methodology:

  • Used anonymized clinical text data from MIMIC-III for fine-tuning.

  • Applied transfer learning on BERT and BioBERT models.

  • Evaluated summaries using ROUGE scores and clinician feedback.

Credit Risk Scoring System with Explainable AI

Objective: To build a transparent and accurate machine learning system to assess loan default risks and assist financial institutions in informed lending decisions.

Key Features and Functionalities:

  • Risk Prediction:
    Developed a credit scoring model using XGBoost and logistic regression based on customer demographic, financial, and credit history data.

  • Explainable Predictions:
    Incorporated SHAP values for interpretability, allowing underwriters to understand key drivers of each prediction.

  • Interactive Visualization:
    Built a Tableau dashboard to visualize credit risk scores, feature importance, and user-specific explanations.

Data and Methodology:

  • Used a structured financial dataset with thousands of loan records and repayment history.

  • Handled class imbalance with SMOTE and validated with stratified k-fold cross-validation.

  • Deployed the model and dashboard via AWS EC2 and RDS.

Real-Time Student Engagement Detector

Objective: To create an intelligent computer vision system that monitors student attention in virtual classrooms and provides real-time feedback to instructors.

Key Features and Functionalities:

  • Engagement Detection:
    Used CNN and OpenCV to analyze facial expressions, blink rate, and head pose from webcam footage.

  • Real-Time Monitoring:
    Streamed engagement scores via a dashboard to alert instructors when student attention dropped.

  • Privacy-Compliant Deployment:
    Deployed locally on edge devices to ensure data privacy and low-latency performance.

Data and Methodology:

  • Collected labeled video datasets representing engaged vs. distracted behaviors.

  • Trained CNN on preprocessed frames; applied Haar cascades and Dlib landmarks.

  • Deployed using Flask + Streamlit.

Multimodal Disease Diagnosis using Tabular + Image Data

Objective: To enhance disease diagnosis accuracy by integrating visual and clinical data in a unified machine learning framework.

Key Features and Functionalities:

  • Multimodal Fusion Model:
    Combined CNN-based image analysis with GBM on structured clinical data using late-fusion architecture.

  • Condition-Specific Diagnosis:
    Focused on complex conditions like diabetic retinopathy and cardiovascular disease where image + tabular data are complementary.

  • User Interface:
    Built an interface to upload data, visualize predictions, and generate clinical summaries.

Data and Methodology:

  • Used 100K+ patient records including medical scans and clinical reports.

  • Trained CNN (ResNet) for image data and GBM for tabular features; merged logits before classification.

  • Conducted cross-modal evaluation and ablation studies.

Large-Scale LLM-Powered Document Retrieval System

Objective: To create a fast and accurate system that retrieves relevant content from millions of enterprise documents using LLMs and semantic search.

Key Features and Functionalities:

  • RAG Architecture:
    Implemented a retrieval-augmented generation pipeline using FAISS and MiniLM for dense vector similarity search.

  • Efficient Indexing:
    Chunked and embedded 1M+ documents with caching and FAISS indexing for low-latency retrieval.

  • Frontend Integration:
    Deployed backend via FastAPI and connected it to a React-based UI for user interaction.

Data and Methodology:

  • Preprocessed diverse enterprise text formats (PDFs, Word, HTML).

  • Used SentenceTransformers (MiniLM) for embedding generation.

  • Benchmarked against keyword-based retrieval using precision and recall.

Smart Defect Detection in Manufacturing using Vision AI

Objective: To automate quality control in manufacturing by detecting surface-level defects on parts using real-time computer vision.

Key Features and Functionalities:

  • Defect Classification:
    Trained a CNN to detect defects such as cracks, dents, and discoloration from camera feeds.

  • Real-Time Inference:
    Deployed using FastAPI and Docker on edge devices for integration with IoT-based production lines.

  • Actionable Output:
    Provided visual alerts and defect localization overlays to aid human operators.

Data and Methodology:

  • Used 50K+ labeled images from industrial cameras.

  • Performed data augmentation and handled class imbalance with weighted loss.

  • Achieved 94% accuracy.

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