COMPLETED PROJECTS

1. 🏥 Predicting Medical Condition from Patient Diagnosis or Review

AI-powered NLP model to classify patient reviews and diagnoses into medical conditions

🚀 Project Overview

Patients often describe their symptoms or medication experiences in text form. This project uses Natural Language Processing (NLP) and Machine Learning to classify these reviews/diagnoses into medical conditions (e.g., Diabetes, Hypertension, Depression).

✨ Key Features

✅ Text Preprocessing: Tokenization, Lemmatization, Stopword Removal
✅ Feature Engineering: TF-IDF, Word Embeddings

✅ ML Models: Logistic Regression, Passive Aggressive Classifier, MultinomialNB for Text Classification
✅ Evaluation Metrics: Accuracy, Confusion Matrix
✅ Deployment: Streamlit Web App for real-time prediction

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📊 Tech Stack

• Language: Python
• Libraries: Pandas, Scikit-Learn, SpaCy, NLTK
• Machine Learning Models: Logistic Regression, Random Forest, LSTM, BERT
• Deployment: Streamlit Web App

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📌 Project Links & Resources

Resource	Link
🔗 GitHub Repository	GitHub Repo
📓 Google Colab Notebook	Colab Notebook
🏆 Demo Web App	Streamlit App

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🔍 How It Works

1️⃣ Data Preprocessing

• Remove special characters, stopwords, and punctuation
• Apply lemmatization using spaCy
• Convert text into numerical vectors using TF-IDF

2️⃣ Model Training

• Baseline Model: TF-IDF + Logistic Regression (benchmark model)
• Advanced Model: BERT-based Transformer for text classification

3️⃣ Evaluation Metrics

• Accuracy: 95%
• Confusion Matrix

4️⃣ Deployment

• Streamlit Web App for real-time predictions

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📊 Sample Predictions

Review Text	Predicted Condition
"I have severe headaches and dizziness daily."	Migraine
"My sugar levels are high even after taking insulin."	Diabetes
"Feeling sad and hopeless for months."	Depression
"Blood pressure spikes every morning."	Hypertension

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