End-to-end machine learning project involving exploratory data analysis, preprocessing, and model development using the U.S. Chronic Disease Indicators dataset. Implements supervised and unsupervised learning techniques, compares model performance using appropriate evaluation metrics, and derives data-driven insights from healthcare data.

This Streamlit app uses AI and ML to analyze report. It supports document uploads for report analysis. The app also uses ML models to classify chronic diseases like brain tumors, kidney stones, and pneumonia.

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Developed a machine learning model to predict Chronic Kidney Disease (CKD) using patient medical data. Implemented Decision Tree, KNN, and Random Forest algorithms, with and without feature selection, and achieved highest accuracy using Random Forest.

Web app deployed ML model for Chronic Kidney Disease Diagnosing using Lab Analysis

• Developed ML models (KNN, Naive Bayes, Logistic Regression, Decision Tree) to classify CKD, achieving 95.5% accuracy with Naive Bayes. • Implemented KNN and Logistic Regression from scratch, with Logistic Regression achieving 92.5%, KNN 90%, and Decision Tree 54.5% accuracy, evaluated using precision, recall, and F1 score.

End-to-end analysis and prediction of Chronic Kidney Disease using Python (EDA, ML models, documentation).

CKD analysis and predictor with ML-orientated approach. Data: https://archive.ics.uci.edu/dataset/336/chronic+kidney+disease

Data-Driven Disease Prediction: A Chronic Risk Analysis predicts hypertension, diabetes, heart disease & CKD using advanced analytics. Enriched from Kaggle’s 100K dataset, it applies PCA, Isolation Forest & RFE with multi-target classification and ML models to yield actionable healthcare insights.

This project analyzes chronic kidney disease data using Python and it includes data preprocessing , cleaning , Exploratory data analysis(EDA) with visualization, Feature selection and scaling , predictive model building and evaluation using ML algorithms.

🔬 Chronic Kidney Disease (CKD) Prediction ML Project utilizing the CKD Dataset. Features data preprocessing (imputation, scaling, encoding), Exploratory Data Analysis (EDA), model training (Logistic Regression, KNN, Random Forest), and hyperparameter tuning (Grid Search) for accurate CKD classification.

End-to-end ML pipeline using the Synthetic Health & Lifestyle Dataset. Features data cleaning, SQL cohort analysis, and predictive modeling for chronic disease and sleep patterns. Includes clinical dashboards and feature importance for healthcare insights. Tech: Python, Scikit-learn, SQL, Seaborn

used Azure ML Studio Designer to build a classification model to predict the likelihood of a patient developing Chronic Heart Disease (CHD) in the coming ten years. Created model pipeline for data cleansing, feature analysis, model evaluation and REST Endpoint deployment, achieving accuracy of 84%.