Best free stroke prediction dataset github. We aim to identify the factors that con.
Best free stroke prediction dataset github Version 2 significantly improves upon Version 1 by incorporating age-dependent symptom probabilities , gender-specific risk modifiers , and medically validated feature engineering . Analysis of the Stroke Prediction Dataset provided on Kaggle. [ ] Mar 7, 2025 · This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, and various diseases and smoking status. Mar 8, 2024 · Here are three potential future directions for the "Brain Stroke Image Detection" project: Integration with Multi-Modal Data:. gender: "Male", "Female" or "Other" age: age of the patient. Reload to refresh your session. o Visualize the relation between stroke and other features by use pandas crosstab and seaborn heatmap. synthea-pt30k-stroke-ml-table-sel-convert. com Data Source: The healthcare-dataset-stroke-data. Contact Info Please direct all communications to Henry Tsai @ hawkeyedatatsai@gmail. - ajspurr/stroke_prediction Perform Extensive Exploratory Data Analysis, apply three clustering algorithms & apply 3 classification algorithms on the given stroke prediction dataset and mention the best findings. Apr 17, 2021 · This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status. By doing so, it also urges medical users to strengthen the motivation of health management and induce changes in their health behaviors. Instant dev environments Part I (see Stroke prediction using Logistic regression. Feature Selection: The web app allows users to select and analyze specific features from the dataset. The model aims to assist in early detection and intervention of strokes, potentially saving lives and improving patient outcomes. A Convolutional Neural Network (CNN) is used to perform stroke detection on the CT scan image dataset. - AkramOM606/DeepLearning-CNN-Brain-Stroke-Prediction Jun 13, 2021 · Download the Stroke Prediction Dataset from Kaggle and extract the file healthcare-dataset-stroke-data. o Convert categorical variables to numbers by LabelEncoder in sklearn. This video showcases the functionality of the Tkinter-based GUI interface for uploading CT scan images and receiving predictions on whether the image indicates a brain stroke or not. The dataset used in the development of the method was the open-access Stroke Prediction dataset. Manage code changes To enhance the accuracy of the stroke prediction model, the dataset will be analyzed and processed using various data science methodologies and algorithm About This data science project aims to predict the likelihood of a patient experiencing a stroke based on various input parameters such as gender, age, presence of diseases, and smoking status. This project uses six machine learning models (XGBoost, Random Forest Classifier, Support Vector Machine, Logistic Regression, Single Decision Tree Classifier, and TabNet)to make stroke predictions. Leveraged skills in data preprocessing, balancing with SMOTE, and hyperparameter optimization using KNN and Optuna for model tuning. DataFrame'> Int64Index: 4908 entries, 0 to 5109 Data columns (total 13 columns): # Column Non-Null Count Dtype --- ----- ----- ----- 0 id 4908 non-null int64 1 gender 4908 non-null object 2 age 4908 non-null float64 3 hypertension 4908 non-null int64 4 heart_disease 4908 non-null int64 5 ever_married 4908 non-null object 6 work_type 4908 non-null object 7 Residence Comparing 10 different ML classifiers and using the one having best accuracy to predict the stroke risk to user. The dataset have: 4 numerical variables: "id", "age", "avg_glucose_leve" and "bmi" The primary goal of this project is to develop a model that predicts the likelihood of a stroke based on input parameters like gender, age, symptoms, and lifestyle factors. This package can be imported into any application for adding security features. It is used to predict whether a patient is likely to get stroke based on the input parameters like age, various diseases, bmi, average glucose level and smoking status. Timely prediction and prevention are key to reducing its burden. Healthalyze is an AI-powered tool designed to assess your stroke risk using deep learning. Kaggle is an AirBnB for Data Scientists. - mmaghanem/ML_Stroke_Prediction Data Set Information: This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, and various diseases and smoking status. Among the records, 1. Brain stroke poses a critical challenge to global healthcare systems due to its high prevalence and significant socioeconomic impact. Summary without Implementation Details# This dataset contains a total of 5110 datapoints, each of them describing a patient, whether they have had a stroke or not, as well as 10 other variables, ranging from gender, age and type of work Hi all,. - sakshitodi/Stroke-Prediction Dec 11, 2022 · This project hence helps to predict the stroke risk using prediction model and provide personalized warning and the lifestyle correction message. Stroke Prediction Dataset If not available on GitHub, the notebook can be accessed on nbviewer, or alternatively on Kaggle. Contribute to 9amomaru/Stroke-Prediction-Dataset development by creating an account on GitHub. This project describes step-by-step procedure for building a machine learning (ML) model for stroke prediction and for analysing which features are most useful for the prediction. Initially an EDA has been done to understand the features and later More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects. Data exploration, preprocessing, analysis and building a stroke model prediction in the life of the patient. Sign in Product This is a repository for code used in Bioengineering Capstone at Stanford (Bioe 141A/B). o Replacing the outlier values with the mode. - JuanS286/StrokeClassifier This project looks to create a stroke classifier to predict the likelihood of a patient to have a stroke. GitHub community articles Skip to content. In the code, we have created the instance of the Flask() and loaded the model. 42 Explanatory Data Analysis -Patients between the age of 50-80 years old are at greater risk of getting a stroke. Stroke prediction project based on the kaggle stroke prediction dataset by Fedesoriano - kkalera/Stroke-Prediction. 3). To prepare the data for modelling, an Exploratory Data Analysis was conducted where it was detected that the dataset is very imbalance (95% no stroke, 5% stroke). Analysis of the Stroke Prediction Dataset to provide insights for the hospital. html is pressed) and converts it into an array. - bpalia/StrokePrediction. 5 never smoked 1 Aug 25, 2022 · More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. model --lrsteps 200 250 --epochs 300 --outbasepath ~/tmp/shape --channelscae 1 16 24 32 100 200 1 --validsetsize 0. py ~/tmp/shape_f3. Machine Learning project using Kaggle Stroke Dataset where I perform exploratory data analysis, data preprocessing, classification model training (Logistic Regression, Random Forest, SVM, XGBoost, KNN), hyperparameter tuning, stroke prediction, and model evaluation. frame. Contribute to orkunaran/Stroke-Prediction development by creating an account on GitHub. Manage code changes Stroke Prediction Analysis Project: This project explores a dataset on stroke occurrences, focusing on factors like age, BMI, and gender. To determine which model is the best to make stroke predictions, I plotte… Find and fix vulnerabilities Codespaces. The API can be integrated seamlessly into existing healthcare systems project aims to predict the likelihood of a stroke based on various health parameters using machine learning models. I have a passion for investigating and analysing data. where P k, c is the prediction or probability of k-th model in class c, where c = {S t r o k e, N o n − S t r o k e}. Prediksi penyakit stroke ini merupakan suatu sistem yang mampu melakukan prediksi yang dapat membantu dalam menilai apakah seorang pasien memiliki atau tidaknya risiko yang signifikan untuk mengidap penyakit stroke, dan hal ini didasarkan pada data medis yang tersedia atau diberikan. The dataset was obtained from Kaggle and contains 5110 rows and 10 columns to detect stroke predictions. A dataset containing all the required fields to build robust AI/ML models to detect Stroke. Specifically, this report presents county (or county equivalent) estimates of heart Write better code with AI Code review. Resources Contribute to arturnovais/Stroke-Prediction-Dataset development by creating an account on GitHub. 3. We get the conclusion that age, hypertension and work type self-employed would affect the possibility of getting stroke. Instant dev environments Write better code with AI Security. 1545). csv │ │ └── stroke_data_final. It provides insights into various factors influencing stroke risk, allowing for binary classification (risk vs. The goal here is to get the best accuracy on a larger dataset. o scale values of avg_glucose_level, bmi, and age by using StandardScaler in sklearn. Each row in the data provides relavant information about the patient. Model performance was evaluated using several metrics suited for imbalanced datasets: Precision: The accuracy of positive predictions. Input data is preprocessed and is given to over 7 models, where a maximum accuracy of 99. This dataset is designed for predicting stroke risk using symptoms, demographics, and medical literature-inspired risk modeling. Marital status and presence of heart disease have no significant effect on stroke; Older age, hypertension, higher glucose level and higher BMI increase the risk of stroke Contribute to arturnovais/Stroke-Prediction-Dataset development by creating an account on GitHub. The output attribute is a 3) What does the dataset contain? This dataset contains 5110 entries and 12 attributes related to brain health. Optimized dataset, applied feature engineering, and implemented various algorithms. This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status. Manage code changes The Nencki-Symfonia EEG/ERP dataset: high-density electroencephalography (EEG) dataset obtained at the Nencki Institute of Experimental Biology from a sample of 42 healthy young adults with three cognitive tasks: (1) an extended Multi-Source Interference Task (MSIT+) with control, Simon, Flanker, and multi-source interference trials; (2) a 3 Stroke and BMI have the strongest correlation with 0. Later tuned model by selecting variables with high coefficient > 0. The dataset includes 100k patient records. AUC-PR measures the area under the precision-recall curve and provides an aggregate measure of model performance for imbalanced datasets. - NIRMAL1508/STROKE-DISEASE-PREDICTION Whether your focus is on predictions or classification, these datasets are not only intriguing but also invaluable for machine learning endeavors. In our project we want to predict stroke using machine learning classification algorithms, evaluate and compare their results. drop(['stroke'], axis=1) y = df['stroke'] 12. You switched accounts on another tab or window. o use SMOTE from The Jupyter notebook notebook. Sep 21, 2021 · <class 'pandas. We used as a dataset the "Stroke Prediction Dataset" from Kaggle. joblib │ │ ├── model_metadata. hypertension: 0 if the patient doesn't have hypertension, 1 if the patient has hypertension Stroke Prediction Dataset. Using SQL and Power BI, it aims to identify trends and corr (Sorry about that, but we can’t show files that are this big right now app. Feature Engineering; o Substituting the missing values with the mean. This dataset was created by fedesoriano and it was last updated 9 months ago. Navigation Menu Toggle navigation To predict what factors influence a person’s stroke, I will utilize the stroke variable as the dependent variable. Stroke are becoming more common among female than male; A person’s type of residence has no bearing on whether or not they have a stroke. ; The system uses a 70-30 training-testing split. 5% of them are related to non-stroke patients. csv from the Kaggle Website, credit to the author of the dataset fedesoriano. This dataset documents rates and trends in heart disease and stroke mortality. Since the dataset is small, the training of the entire neural network would not provide good results so the concept of Transfer Learning is used to train the model to get more accurate resul Saved searches Use saved searches to filter your results more quickly AUC-PR: The Neural Network model has a slightly higher AUC-PR score (0. This project utilizes ML models to predict stroke occurrence based on patient demographic, medical, and lifestyle data. As said above, there are 12 features with one target feature or response variable -stroke- and 11 explanatory variables. Navigation Menu Toggle navigation Plan and track work Code Review. It also has a precision of 100% meaning our model can predict 100% of the time patients that don't have stroke A ML model which predicts the probability of a person having a stroke along with the key factors which play a major role in causing a stroke. The objective is to predict brain stroke from patient's records such as age, bmi score, heart problem, hypertension and smoking practice. With over 9 years in the data analysis field, I am currently pursuing a degree in this area to further my skills and knowledge. - GitHub - erma0x/stroke-prediction-model: Data exploration, preprocessing, analysis and building a stroke model prediction in the life of the patient. machine-learning random-forest svm jupyter-notebook logistic-regression lda knn baysian stroke-prediction cerebral stroke prediction based on imbalanced medical dataset - Jdss026/stroke-classifier. 3. Dataset: Stroke Prediction Dataset Prediction of stroke in patients using machine learning algorithms. to make predictions of stroke cases based on simple health 11 clinical features for predicting stroke events Stroke Prediction Dataset | Kaggle Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Contribute to renjinirv/Stroke-prediction-dataset development by creating an account on GitHub. The dataset consists of 11 clinical features which contribute to stroke occurence. 92 32. 1906) compared to the XGBoost model (0. A subset of the original train data is taken using the filtering method for Machine Learning and Data Visualization purposes. 21 N/A never smoked 1 Male 80 0 1 Yes Private Rural 105. Project Overview This project focuses on detecting brain strokes using machine learning techniques, specifically a Convolutional Neural Network (CNN) algorithm. The whole code is built on different Machine learning techniques and built on website using Django machine-learning django random-forest logistic-regression decision-trees svm-classifier knn-classification navies-bayes-classifer heart-disease-prediction kidney-disease-prediction You signed in with another tab or window. To get started In this project, we used logistic regression to discover the relationship between stroke and other input features. Achieved high recall for stroke cases. You signed out in another tab or window. . Stacking [] belongs to ensemble learning methods that exploit several heterogeneous classifiers whose predictions were, in the following, combined in a meta-classifier. Find and fix vulnerabilities Predicting whether a patient is likely to get stroke or not - terickk/stroke-prediction-dataset In this stroke prediction model we have implemented Logistic Regression, Random Forest & LightGBM. Dependencies Python (v3. Context According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for Introduction¶ The dataset for this competition (both train and test) was generated from a deep learning model trained on the Stroke Prediction Dataset. These datasets were used to simulate ML-LHS in the Nature Sci Rep paper. Contribute to TomerSh135/Stroke-Prediction-Dataset development by creating an account on GitHub. csv │ │ ├── stroke_data_engineered. We have also done Hyperparameter tuning for each model. 3 The project uses machine learning to predict stroke risk using Artificial Neural Networks, Decision Trees, and Naive Bayes algorithms. Manage code changes Skip to content Find and fix vulnerabilities Codespaces. heroku scikit-learn prediction stroke-prediction Predicting whether a patient is likely to get stroke or not - stroke-prediction-dataset/README. - hridaybasa/Stroke-Prediction-Using-Data-Science-And-Machine-Learning The system uses data pre-processing to handle character values as well as null values. 4) Which type of ML model is it and what has been the approach to build it? This is a classification type of ML model. We did the following tasks: Performance Comparison using Machine Learning Classification Algorithms on a Stroke Prediction dataset. Sign in Product Brain Stroke Prediction- Project on predicting brain stroke on an imbalanced dataset with various ML Algorithms and DL to find the optimal model and use for medical applications. Issues are used to track todos, bugs, feature requests, and more. Feel free to use the original dataset as part of this competition PREDICTION-STROKE/ ├── data/ │ ├── models/ │ │ ├── best_stroke_model. Stroke prediction with machine learning and SHAP algorithm using Kaggle dataset - Silvano315/Stroke_Prediction. md at main · terickk/stroke-prediction-dataset The dataset used to predict stroke is a dataset from Kaggle. Contribute to SourcM/health-care-dataset development by creating an account on GitHub. For modeling, the categorical features where encoded, XGBoost was use das Predicting whether a patient is likely to get stroke or not - stroke-prediction-dataset/code. Contribute to Aftabbs/Stroke-Prediction-using-Machine-Learning development by creating an account on GitHub. Dealing with Class Imbalance. Mar 22, 2023 · gender age hypertension heart_disease ever_married work_type Residence_type avg_glucose_level bmi smoking_status stroke Female 61 0 0 Yes Self-employed Rural 202. ; The system uses Logistic Regression: Logistic Regression is a regression model in which the response variable (dependent variable) has categorical values such as True/False or 0/1. We aim to identify the factors that con Navigation Menu Toggle navigation. Analysis based 4 different machine learning models. joblib │ │ └── optimized_stroke_model. Deployment and API: The stroke prediction model is deployed as an easy-to-use API, allowing users to input relevant health data and obtain real-time stroke risk predictions. I used a KNN to make the Stroke predictions. Stacking. Task: To create a model to determine if a patient is likely to get a stroke based on the parameters provided. Dataset Overview: The web app provides an overview of the Stroke Prediction dataset, including the number of records, features, and data types. ipynb contains the model experiments. Our model peformed amazingly having a recall of 100% meaning that our model can predict 100% of the time patients with stroke. py has the main function and contains all the required functions for the flask app. I used Logistic Regression with manual class weights since the dataset is imbalanced. For this purpose, I used the "healthcare-dataset-stroke-data" from Kaggle. We analyze a stroke dataset and formulate advanced statistical models for predicting whether a person has had a stroke based on measurable predictors. 7) Libraries Used: Pandas, Scitkitlearn, Keras, Tensorflow, MatPlotLib, Seaborn, and NumPy DataSet Description: The Kaggle stroke prediction dataset contains over 5 thousand samples with 11 total features (3 continuous) including age, BMI, average glucose level, and more. We intend to implement a prototype that senses relevant parameters and need not necessarily be wearable The Stroke Risk Prediction Dataset is a comprehensive dataset designed for machine learning and medical research purposes. Using SQL and Power BI, it aims to identify trends and corr This project predicts stroke disease using three ML algorithms - fmspecial/Stroke_Prediction The Dataset Stroke Prediction is taken in Kaggle. - GitHub - sa-diq/Stroke-Prediction: Prediction of stroke in patients using machine learning algorithms. Sign in Product The aim of this project is to determine the best model for the prediction of brain stroke for the dataset given, to enable early intervention and preventive measures to reduce the incidence and impact of strokes, improving patient outcomes and overall healthcare. One dataset after value conversion. no risk) and regression (risk percentage prediction). The best-performing model is deployed in a web-based application, with future developments including real-time data integration. Write better code with AI Code review. Contribute to Rasha-A21/Stroke-Prediction-Dataset development by creating an account on GitHub. There are more female than male in the data set. Standard codes for the stroke data: synthea-stroke-dataset-codes. Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. and choosign the best one (for this case): the Stroke Prediction Analysis Project: This project explores a dataset on stroke occurrences, focusing on factors like age, BMI, and gender. This project builds a classifier for stroke prediction, which predicts the probability of a person having a stroke along with the key factors which play a major role in causing a stroke. GitHub Copilot. x = df. The "Cerebral Stroke Prediction" dataset is a real-world dataset used for the task of predicting the occurrence of cerebral strokes in individual. The goal is to develop a robust model for stroke prediction. Using the “Stroke Prediction Dataset” available on Kaggle, our primary goal for this project is to delve deeper into the risk factors associated with stroke. Sign in Product With a relatively smaller dataset (although quite big in terms of a healthcare facility), every possible effort to minimize or eliminate overfitting was made, ranging from methods like k-fold cross validation to hyperparameter optimization (using grid search CV) to find the best value for each parameters in a model. Future Direction: Incorporate additional types of data, such as patient medical history, genetic information, and clinical reports, to enhance the predictive accuracy and reliability of the model. They offer relatively clean data, well-suited for machine learning tasks, with an abundance of variables to aid in making predictions for the target column. Stroke Prediction for Preventive Intervention: Developed a machine learning model to predict strokes using demographic and health data. This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. The project aims at displaying the charts/plots of the number of people affected by stroke based on the input parameters like smoking status, high blood pressure level, Cholesterol level, obesity level in some of the countries. GitHub repository for stroke prediction project. 3 --fold 17 6 2 26 11 4 1 21 16 27 24 18 9 22 12 0 3 8 23 25 7 10 19 Selected features using SelectKBest and F_Classif. It’s a crowd- sourced platform to attract, nurture, train and challenge data scientists from all around the world to solve data science, machine learning and predictive analytics problems. Prediction of brain stroke based on imbalanced dataset in Stroke Prediction Dataset Context According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. It includes data preprocessing (label encoding, KNN imputation, SMOTE for balancing), and trains models like Naive Bayes, Decision Tree, SVM, and Logistic Regression. Activate the above environment under section Setup. Tools: Jupyter Notebook, Visual Studio Code, Python, Pandas, Numpy, Seaborn, MatPlotLib, Supervised Machine Learning Binary Classification Model, PostgreSQL, and Tableau. ipynb at main · terickk/stroke-prediction-dataset Write better code with AI Code review. Feature distributions are close to, but not exactly the same, as the original. Predicted stroke risk with 92% accuracy by applying logistic regression, random forests, and deep learning on health data. This project predicts stroke occurrences using machine learning on a healthcare dataset. I have done EDA, visualisation, encoding, scaling and modelling of dataset. For learning the shape space on the manual segmentations run the following command: train_shape_reconstruction. 9. joblib │ ├── processed/ │ │ ├── processed_stroke_data. ipynb - 4. 5% of them are related to stroke patients and the remaining 98. 1. using visualization libraries, ploted various plots like pie chart, count plot, curves Stroke Disease Prediction classifies a person with Stroke Disease and a healthy person based on the input dataset. ; Recall: The ability of the model to capture actual positive instances. csv Synthetically generated dataset containing Stroke Prediction metrics. DataFrame'> Int64Index: 4088 entries, 25283 to 31836 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ----- ----- ----- 0 gender 4088 non-null object 1 age 4088 non-null float64 2 hypertension 4088 non-null int64 3 heart_disease 4088 non-null int64 4 ever_married 4088 non-null object 5 work_type 4088 non-null object 6 Residence_type 4088 non-null Navigation Menu Toggle navigation. core. The dataset is preprocessed, analyzed, and multiple models are trained to achieve the best prediction accuracy. 15,000 records & 22 fields of stroke prediction dataset, containing: 'Patient ID', 'Patient Name', 'Age', 'Gender', 'Hypertension', 'Heart Disease', 'Marital Status', 'Work Type Navigation Menu Toggle navigation. Fetching user details through web app hosted using Heroku. csv │ └── raw/ │ └── healthcare-dataset <class 'pandas. 4% is achieved. - Issues · KSwaviman/EDA-Clustering-Classification-on-Stroke-Prediction-Dataset In our project we want to predict stroke using machine learning classification algorithms, evaluate and compare their results. Mechine Learnig | Stroke Prediction. Nov 21, 2023 · Didn’t eliminate the records due to dataset being highly skewed on the target attribute – stroke and a good portion of the missing BMI values had accounted for positive stroke; The dataset was skewed because there were only few records which had a positive value for stroke-target attribute Stroke Prediction Using Machine Learning (Classification use case) Topics machine-learning model logistic-regression decision-tree-classifier random-forest-classifier knn-classifier stroke-prediction Skip to content. Stroke Prediction Dataset. It gives users a quick understanding of the dataset's structure. Manage code changes Contribute to CTrouton/Stroke-Prediction-Dataset development by creating an account on GitHub. predict() method takes input from the request (once the 'compute' button from index. Sep 15, 2022 · We set x and y variables to make predictions for stroke by taking x as stroke and y as data to be predicted for stroke against x. With a relatively smaller dataset (although quite big in terms of a healthcare facility), every possible effort to minimize or eliminate overfitting was made, ranging from methods like k-fold cross validation to hyperparameter optimization (using grid search CV) to find the best value for each parameters in a model. Hi all, This is the capstone project on stroke prediction dataset. Find and fix vulnerabilities Perform Extensive Exploratory Data Analysis, apply three clustering algorithms & apply 3 classification algorithms on the given stroke prediction dataset and mention the best findings. Navigation Menu Toggle navigation. Write better code with AI Security. Healthalyze is an AI-powered tool designed to assess your stroke risk using deep learning. Manage code changes The dataset used in this project contains information about various health parameters of individuals, including: id: unique identifier; gender: "Male", "Female" or "Other" Welcome to my portfolio. - NVM2209/Cerebral-Stroke-Prediction Write better code with AI Code review. Contribute to kushal3877/Stroke-Prediction-Dataset development by creating an account on GitHub. With just a few inputs—such as age, blood pressure, glucose levels, and lifestyle habits our advanced CNN model provides an accurate probability of stroke occurrence. id: unique identifier. This dataset has: 5110 samples or rows; 11 features or columns; 1 target column (stroke). Additionally, the project aims to analyze the dataset to identify the most significant features that contribute to stroke prediction. As issues are created, they’ll appear here in a searchable and filterable list. using visualization libraries, ploted various plots like pie chart, count plot, curves The object is to use the best machine learning model and come back to study the correct predictions, and find out more precious characters on stroke patients. Stroke ML datasets from 30k to 150k Synthea patients, available in Harvard Dataverse: Synthetic Patient Data ML Dataverse. csv. - GitHub - Assasi Model comparison techniques are employed to determine the best-performing model for stroke prediction. Dataset yang Write better code with AI Code review. - KSwaviman/EDA-Clustering-Classification-on-Stroke-Prediction-Dataset Heart disease prediction and Kidney disease prediction. Code in this repository is used for testing of methods for predicting heat stroke with a wearable monitor. model. This dataset has been used to predict stroke with 566 different model algorithms. The dataset for the project has the following columns: id: unique identifier; gender: "Male", "Female" or "Other" age: age of the patient; hypertension: 0 if the patient doesn't have hypertension, 1 if the patient has hypertension Stroke prediction. rtbhrhtgnybkinzqfqiedryyftpflcyudmmojijmjsklcdjnarblvbqdsoslbmdzlngwgiqhl
