Topic: heartattack Goto Github
Some thing interesting about heartattack
Some thing interesting about heartattack
heartattack,This is a Machine Learning web app developed using Python and StreamLit. Uses algorithms like Logistic Regression, KNN, SVM, Random Forest, Gradient Boosting, and XGBoost to build powerful and accurate models to predict the status of the user (High Risk / Low Risk) with respect to Heart Attack and Breast Cancer.
User: advikmaniar
heartattack,Predictive Analysis on the ERFastTrack data to predict whether based on the symptoms the patient is going to get a heart attack.
User: avijay24
heartattack,Analyzing heart attack with respect to given feature and building a predictive model for finding out if a person will suffer from a heart attack or not.
User: bibhuprasadpanda97
Home Page: https://www.kaggle.com/rashikrahmanpritom/heart-attack-analysis-prediction-dataset
heartattack,Heart Attack Prediction
User: devanshu0502
heartattack,Random Forest is a powerful tool in healthcare, helping predict heart attack fatalities. It analyzes diverse patient data, creating an ensemble of decision trees, each with unique insights. By combining these trees, it offers a more accurate risk assessment for heart attack death, potentially saving lives.
User: elzafr
heartattack,This project involves structuring a heart attack risk dataset from Kaggle into a relational SQL database with multiple tables, setting primary and foreign keys for data integrity, and adjusting data types for optimized analysis and application use.
User: lsawfa
heartattack,Project on anomaly detection using statistical learning.
User: markielokie
heartattack,This repository contains all the Projects I lay my hands on as a Kaggle BIPOC Grantee via Kaggle learn and other sources made available to us. Thanks, Kaggle BIPOC Grant team!!
User: maryadewunmi
heartattack,Streamlit web app to early predict heart attack
User: mustafarezk12
Home Page: https://heartattackpresiction-2hayrzmy2qctneqdzt4hux.streamlit.app/
heartattack,In this notebook, I have reviewed several classification algorithms on datasets.
User: raziyeakr
heartattack,A machine learning project focused on predicting heart attacks using models like Logistic Regression, Random Forest, and XGBoost, achieving an 83.61% test accuracy. Includes comprehensive EDA, feature engineering, and hyperparameter tuning.
User: travelxml
heartattack,"Heart Attack Analysis" - A data science project for predicting heart attacks using machine learning on health-related data.
User: uditmahato
heartattack,Predict whether patients are at high or low risk of heart attacks
User: zeytrack
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