A Python project that analyzes medical examination data and generates insightful visualizations. It produces categorical bar plots and correlation heatmaps to explore health-related metrics.
The purpose of the study was to look at the causal relationships between lipid profiles (TG, HDL, and LDL) and diseases, drugs, and lifestyle choices. MR analysis was used and applied to GWAS from the UK Biobank to examine these associations using analysis software RStudio.
SEHAT is an interactive Predictive Health Application using Streamlit and machine learning models to forecast diabetes and heart disease risks based on user health parameters. Enhance your health awareness with immediate insights and proactive management tools.
This project aims to leverage machine learning models to predict the likelihood of various diseases based on user inputs. The diseases covered in this system include Diabetes, Heart Disease, and Parkinson's Disease.
Exploratory Data Analysis of the Breast Cancer Wisconsin (Diagnostic) dataset, uncovering patterns that separate benign and malignant tumors.
Our innovative fitness tracker leverages machine learning algorithms to provide personalized insights and recommendations, empowering you to achieve your fitness goals. Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials
Covid -19 Tableau Dashboard WHO (5th January 2020 - 3rd March 2024)
This area will be used to hold all completed assignments for HSC-4933
Exploratory Data Analysis (EDA) of the leading causes of death in the United States (1999–2017), revealing national and state-level mortality trends and insights using NCHS data.
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