Shajitha Parveen A

Motivated and quick-learning graduate with strong problem-solving skills, a foundation in data analysis and programming, and a passion for driving results through technology and teamwork.

Dialysis Complications Prediction System :

 Languages and Tools : Python, Scikit-learn, Google Gemini API, Gradio, Pandas, NumPy

- Engineered a clinical decision support system for early detection of dialysis-related complications.

- Built supervised ML models (Random Forest, AdaBoost, Gradient Boosting, Logistic Regression) for multi-class classification.

- Performed data cleansing, feature importance analysis, and cross-validation.

- Integrated GenAI (Gemini API) to personalize treatment recommendations.

- Developed an interactive Gradio-based web app for real-time usage by clinicians.

Liver Disease Prediction System:

Languages and Tools : Python, Streamlit, XGBoost, Scikit-learn, Matplotlib

- Designed a predictive analytics tool to detect liver diseases using medical datasets.

- Implemented ML models (Logistic Regression, SVM, KNN, XGBoost) with hyperparameter tuning.

- Achieved 97% prediction accuracy and deployed via Streamlit for user interaction.

- Conducted exploratory data analysis (EDA) and presented results using visual plots.