Free stroke prediction dataset github python. o Replacing the outlier values with the mode.

Free stroke prediction dataset github python The Dataset Stroke Prediction is taken in Kaggle. machine-learning-algorithms ml kaggle-dataset heart-attack The Brain Stroke Prediction project has the potential to significantly impact healthcare by aiding medical professionals in identifying individuals at high risk of stroke. 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. Contribute to Vikram3003/Stroke-Analysis-and-Prediction-Python development by creating an account on GitHub. Project Overview This project focuses on detecting brain strokes using machine learning techniques, specifically a Convolutional Neural Network (CNN) algorithm. By inputting relevant health data such as age, blood pressure, cholesterol levels, and lifestyle factors, the app utilizes predictive algorithms to calculate the user's likelihood of having a stroke. Dataset. It includes the following columns: id: Unique identifier for each patient. This code is implementation for the - A. It primarily focuses on data preprocessing, feature engineering, and model training us Aug 25, 2022 · More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. Impact: Practice with imbalanced datasets. Instant dev environments Brain strokes are a leading cause of disability and death worldwide. 2 Performed Univariate and Bivariate Analysis to draw key insights. 60%. 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 The dataset used to build our model is Stroke Prediction Dataset which is available in Kaggle. 162 is just 4% of sample, however we will fill this null Jun 2, 2021 · This is a Stroke Prediction Model. Instant dev environments Stroke prediction. o Visualize the relation between stroke and other features by use pandas crosstab and seaborn heatmap. - ansonnn07/stroke-prediction Model trained on Stroke Dataset. o Replacing the outlier values with the mode. The dataset used in this study underwent extensive data pre-processing, including handling missing values, variable conversion, and data scaling. ipynb — This contains code for the machine learning model to predict heart disease based on the class. - Akshit1406/Brain-Stroke-Prediction A stroke occurs when the brain gets damaged as a result of interruption of the blood supply. This dataset has been used to predict stroke with 566 different model algorithms. Learn more Activate the above environment under section Setup. Automate any workflow Stroke Prediction Using Machine Learning (Classification use case) Topics machine-learning model logistic-regression decision-tree-classifier random-forest-classifier knn-classifier stroke-prediction Write better code with AI Security. Stroke Prediction for Preventive Intervention: Developed a machine learning model to predict strokes using demographic and health data. A Data Science project which predicts stroke using python - pelinsugok/Stroke-Prediction. Standard codes for the stroke data: synthea-stroke-dataset-codes. 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. e. With the growing use of technology in medicine, electronic health records (EHR) provide valuable data for improving diagnosis and patient management. main Jun 24, 2022 · For the purposes of this article, we will proceed with the data provided in the df variable. Mathew and P. Find and fix vulnerabilities Codespaces. Instructions for Using the Prediction System Prerequisites: Ensure the "healthcare-dataset-stroke-data. o Convert categorical variables to numbers by LabelEncoder in sklearn. Read dataset then pre-processed it along with handing missing values and outlier. While the vision workflow aims to train an image classifier that takes in contrast-enhanced spectral mammography (CESM) images, the natural language processing (NLP) workflow aims to train a document classifier that takes in annotation notes about a patient’s symptoms. 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. Instant dev environments The dataset for this project originates from the Kaggle Playground Series, Season 3, Episode 2. The dataset consists of over $5000$ individuals and $10$ different input variables that we will use to predict the risk of stroke. Find and fix vulnerabilities Dataset can also be found in this repository with the path . Multiple disease 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. 7) Age has correlations to bmi, hypertension, heart_disease, avg_gluclose_level, and stroke; All categories have a positive correlation to each other (no negatives) Data is highly unbalanced; Changes of stroke increase as you age, but people, according to this data, generally do not have strokes. Automate any workflow Here we developed a python program for predicting the stroke using KNN algorithm. Early prediction of stroke risk can help in taking preventive measures. Instant dev environments This project analyzes the Heart Disease dataset from the UCI Machine Learning Repository using Python and Jupyter Notebook. csv │ │ ├── stroke_data_engineered. Here, we try to improve the diagnostic/treatment process. Python program for analysing a healthcare dataset on strokes. Data Preprocessing: AWS Data Wrangler is used to preprocess the data. Prediction of brain stroke based on imbalanced dataset in 3) What does the dataset contain? This dataset contains 5110 entries and 12 attributes related to brain health. Dependencies Python (v3. Navigation Menu Toggle navigation Write better code with AI Security. csv" file is available in the working directory. 3. This is a repository for code used in Bioengineering Capstone at Stanford (Bioe 141A/B). Our task is to predict whether a patient will suffer a stroke or not given the medical data of that patient. Instant dev environments 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. py a python script to train a model; model_n=40. no risk) and regression (risk percentage prediction). Contribute to hassaanali18/Stroke-Prediction development by creating an account on GitHub. 27% uisng GA algorithm and it out perform paper result 96. 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 A stroke prediction app using Streamlit is a user-friendly tool designed to assess an individual's risk of experiencing a stroke. The Jupyter notebook notebook. Dec 28, 2024 · Write better code with AI Security. gender: Gender of the patient (Male/Female/Other) Whether your focus is on predictions or classification, these datasets are not only intriguing but also invaluable for machine learning endeavors. joblib │ │ └── optimized_stroke_model. Find and fix vulnerabilities Stroke prediction project based on the kaggle stroke prediction dataset by Fedesoriano - kkalera/Stroke-Prediction. Load the dataset using pandas: disease_data = pd. Stroke is a disease that affects the arteries leading to and within the brain. GitHub repository for stroke prediction project. Achieved high recall for stroke cases. 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. By doing so, it also urges medical users to strengthen the motivation of health management and induce changes in their health behaviors. Optimized dataset, applied feature engineering, and implemented various algorithms. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. As The dataset i used was not my own work i have not included it within this repository. the healthcare sector using Python. Objective: Create a machine learning model predicting patients at risk of stroke. In this project, we replicate a research study Skip to content. This project demonstrates the manual implementation of Machine Learning (ML) models from scratch using Python. The goal of this project is to predict the likelihood of a person having a stroke based on various demographic, lifestyle, and medical factors. In handling of this biased report, Synthetic Minority Oversampling Technique (SMOTE) model was deployed on the dataset to create a synthetic balance between both classes of output. - baisali14/Hypertension-Heart-Disease-and-Stroke-Prediction-using-SVM This repository holds a machine learning model trained using SVM to predict whether a person has hypertension or not, the person has heart disease or not and the person has stroke Find and fix vulnerabilities Codespaces. An application I made during university using a stroke dataset. Code in this repository is used for testing of methods for predicting heat stroke with a wearable monitor. - bpalia/StrokePrediction. It takes different values such as Glucose, Age, Gender, BMI etc values as input and predict whether the person has risk of stroke or not. Developed using libraries of Python and Decision Tree Algorithm of Machine learning. Write better code with AI Security. ) Prediction probability: calculating the prediction probability for the test set. Project Overview: Dataset predicts stroke likelihood based on patient parameters (gender, age, diseases, smoking). A stroke occurs when a blood vessel that carries oxygen and nutrients to the brain is either blocked by a clot or ruptures. Instant dev environments In this project, I use the Heart Stroke Prediction dataset from WHO to predict the heart stroke. py ~/tmp/shape_f3. It uses the Stroke Prediction Dataset found on Kaggle. In our project we want to predict stroke using machine learning classification algorithms, evaluate and compare their results. This project utilizes the Stroke Prediction Dataset from Kaggle, available here. 335 Python 71 HTML movielens-dataset diabetes The dataset provides the patients’ information. Recall is very useful when you have to Our Heart Stroke Prediction project utilizes machine learning algorithms to predict the likelihood of a person having a stroke based on various risk factors. A subset of the original train data is taken using the filtering method for Machine Learning and Data Visualization purposes. Feature Engineering; o Substituting the missing values with the mean. It gives users a quick understanding of the dataset's structure. Leveraged skills in data preprocessing, balancing with SMOTE, and hyperparameter optimization using KNN and Optuna for model tuning. This enhancement shows the effectiveness of PCA in optimizing the feature selection process, leading to significantly better performance compared to the initial accuracy of 61. /Stroke_analysis1 - Stroke_analysis1. 1. Instant dev environments Download Open Datasets on 1000s of Projects + Share Projects on One Platform. In this repo, I utilize Python's scikit-learn and machine learning techniques to predict medical outcomes, specifically strokes. StorkeScan is an advanced health data analysis tool designed to predict and reduce the risk of strokes using data analytics and machine learning techniques. The model used for predictions is trained on a dataset of healthcare records. py has the main function and contains all the required functions for the flask app. joblib │ │ ├── model_metadata. csv was read into Data Extraction. 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. csv │ └── raw/ │ └── healthcare-dataset The Stroke Risk Prediction Dataset is a comprehensive dataset designed for machine learning and medical research purposes. Find and fix vulnerabilities Actions. csv dataset; Pipfile and Pipfile. Take it to the Real World: We need to use our model to make predictions using unseen data to see how it performs. synthea-pt30k-stroke-ml-table-sel-convert. using visualization libraries, ploted various plots like pie chart, count plot, curves The KNDHDS dataset that the authors used might have been more complex than the dataset from Kaggle and the study’s neural network architecture might be overkill for it. These datasets were used to simulate ML-LHS in the Nature Sci Rep paper. For modeling, the categorical features where encoded, XGBoost was use das Stroke has a serious impact on individuals and healthcare systems, making early prediction crucial. Dec 11, 2022 · This project hence helps to predict the stroke risk using prediction model and provide personalized warning and the lifestyle correction message. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects. It employs NumPy and Pandas for data manipulation and sklearn for dataset splitting to build a Logistic Regression model for predicting heart disease. The competition provides a synthetic dataset that was generated from a deep learning model trained on the Stroke Prediction Dataset. Our project is entitled: "Prediction of brain tissues hemodynamics for stroke patients using computed tomography perfusion imaging and deep learning" Find and fix vulnerabilities Codespaces. This project focuses on building a Brain Stroke Prediction System using Machine Learning algorithms, Flask for backend API development, and React. age, average glucose level, and hypertension). The code and open source algorithms I will be working with are written in Python, an extremely popular, well supported, and evolving data analysis language. machine-learning random-forest svm jupyter-notebook logistic-regression lda knn baysian stroke-prediction python java api machine-learning machine-learning-algorithms android-application logistic-regression android-studio android-app flask-api kaggle-dataset volley-library numpy-library sklearn-library heart-disease-predictor heart-disease-prediction Predicted stroke risk with 92% accuracy by applying logistic regression, random forests, and deep learning on health data. The dataset is already provided in the repository . The output attribute is a The dataset used in the development of the method was the open-access Stroke Prediction dataset. Focuses on data preprocessing, model evaluation, and insights interpretation to identify patterns in patient data and build predictive models. There are 12 primary features describing the dataset with one feature being the target variable. This dataset was created by fedesoriano and it was last updated 9 months ago. bin binary file with trained model and dictvectorizer; healthcare-dataset-stroke-data. read_csv('healthcare-dataset-stroke-data. The model is trained on a dataset of patient information and various health metrics to predict the likelihood of an individual experiencing a stroke. electronic-health-record sepsis interpretable-deep-learning mortality-prediction pattern-attention value-embedding It predicts depression by analyzing tweets with SVM, Logistic Regression, Decision Trees, and NLTK in Python. to make predictions of stroke cases based on simple health In our project we want to predict stroke using machine learning classification algorithms, evaluate and compare their results. train. o scale values of avg_glucose_level, bmi, and age by using StandardScaler in sklearn. Stroke ML datasets from 30k to 150k Synthea patients, available in Harvard Dataverse: Synthetic Patient Data ML Dataverse. By analyzing medical and lifestyle-related data, the model helps identify individuals at risk of stroke. By analyzing medical records and identifying key indicators, our model can help healthcare professionals identify patients who are at high risk and take proactive measures to prevent The goal of this ML model is to figure out if a person will experience a stroke on the basis of age, nature of work, urban/rural residency, marital status, and several clinical parameters. The dataset was adjusted to only include adults (Age >= 18) because the risk factors associated with stroke in adolescents and children, such as genetic bleeding disorders, are not captured by this dataset. Code and Datasets for the paper "An Interpretable Risk Prediction Model for Healthcare with Pattern Attention", published on BMC Medical Informatics and Decision Making. It provides insights into various factors influencing stroke risk, allowing for binary classification (risk vs. drop(['stroke'], axis=1) y = df['stroke'] 12. The project utilizes the Flask framework in Python to create the API endpoints, allowing seamless interaction with the model. One dataset after value conversion. Dataset: Stroke Prediction Dataset Brain stroke prediction using machine learning machine-learning logistic-regression beginner-friendly decision-tree-classifier kaggle-dataset random-forest-classifier knn-classifier commented introduction-to-machine-learning xgboost-classifier brain-stroke brain-stroke-prediction This project aims to build a stroke prediction model using Python and machine learning techniques. joblib │ ├── processed/ │ │ ├── processed_stroke_data. ipynb as a Pandas DataFrame; Columns where the BMI value was "NaN" were dropped from the DataFrame 11 clinical features for predicting stroke events Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Contribute to CTrouton/Stroke-Prediction-Dataset development by creating an account on GitHub. In the Heart Stroke dataset, two class is totally imbalanced and heart stroke datapoints will be easy to ignore to compare with the no heart stroke datapoints. csv. Model Evaluation – Assess model performance using accuracy, precision, recall, F1-score, and confusion matrix. This project analyses the various factors of a health records dataset downloaded from Kaggle. We did the following tasks: Performance Comparison using Machine Learning Classification Algorithms on a Stroke Prediction dataset. GitHub Copilot. Data has null value: BMI column has 162 null values. csv; The dataset description is as follows: The dataset consists of 4798 records of patients out of which 3122 are males and 1676 are females. This dataset has: 5110 samples or rows; 11 features or columns; 1 target column (stroke). Steps: Import the necessary libraries: NumPy, pandas, seaborn, scikit-learn. Analysis of the Stroke Prediction Dataset to provide insights for the hospital. 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. In this project, we will attempt to classify stroke patients using a dataset provided on Kaggle: Kaggle Stroke Dataset. The dataset I work with contains history of patient visits with respective time points in addition to patient demographics. - Priyansh42/Stroke-Blood-Clot-Classification Machine Learning project for stroke prediction analysis using clustering and classification techniques. Data Source: The healthcare-dataset-stroke-data. Data Jun 12, 2024 · This code provides the Matlab implementation that detects the brain tumor region and also classify the tumor as benign and malignant. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. Navigation Menu Toggle navigation This was a project for the graduate course Applied Data Mining and Analytics in Business. The app allows users to input relevant health and demographic details to predict the likelihood of having a stroke. Key features of the dataset include attributes related to various aspects of an individual's health, demographics This GitHub repository contains the code for a Stroke Prediction App. The outcome suggested a heavily imbalanced dataset as the accuracy was biased towards the "0" class as many samples in the datset were of no stroke potency. py — This contains Flask APIs that receives cells details through GUI or API calls, computes the predicted value based on our model and returns it Data analysis on Dataset of patients who had a stroke (Sklearn, pandas, seaborn) - panosarv/stroke-prediction Navigation Menu Toggle navigation. predict() method takes input from the request (once the 'compute' button from index. Sign in Dataset Overview: The web app provides an overview of the Stroke Prediction dataset, including the number of records, features, and data types. The dataset used is healthcare-dataset-stroke-data. We tune parameters with Stratified K-Fold Cross Validation, ROC-AUC, Precision-Recall Curves and feature importance analysis. Contribute to haoyu-jia/Stroke-Prediction development by creating an account on GitHub. Fonte: Data for: A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical-datasets Análise exploratória da base de dados Visualização da base, levantamento de perguntas, tratamento da base, tratamento de outliers 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 Find and fix vulnerabilities Codespaces. csv'). js for the frontend. Find and fix vulnerabilities About. I have considered the problem of predicting the chances of a patient having a stroke, and for this, I have used healthcare dataset from Kaggle. ) The data used in this notebook is a stroke prediction dataset. py a python script to create a web service based on the model Predicting whether a patient is likely to get stroke or not - stroke-prediction-dataset/README. Before we proceed to build our machine learning model, we must begin with an exploratory data analysis that will allow us to find any inconsistencies in our data, as well as overall visualization of the dataset. csv from the Kaggle Website, credit to the author of the dataset fedesoriano. csv The objective of this project is to compare the performance of Logistic Regression, Random Forest, and SVM models in predicting stroke risk. Stroke Prediction Dataset. Tools: Jupyter Notebook, Visual Studio Code, Python, Pandas, Numpy, Seaborn, MatPlotLib, Supervised Machine Learning Binary Classification Model, PostgreSQL, and Tableau. For learning the shape space on the manual segmentations run the following command: train_shape_reconstruction. The improved model, which uses PCA instead of the genetic algorithm (GA) previously mentioned, achieved an accuracy of 97. This model differentiates between the two major acute ischemic stroke (AIS) etiology subtypes: cardiac and large artery atherosclerosis enabling healthcare providers to better identify the origins of blood clots in deadly strokes. - mmaghanem/ML_Stroke_Prediction Toggle navigation. Skip to content. Navigation Menu Toggle navigation Machine Learning Model as Python Package "stroke-pred-p0w11' Data Storage unit using PostgresSQl & Sqlalchmey Data Ingestion job using Airflow to collect our data based on the user inputs. 4. html is pressed) and converts it into an array. Incorporate more data: To improve our dataset in the next iterations, we need to include more data points of people with stroke so that we can create target balance before modeling Contribute to CTrouton/Stroke-Prediction-Dataset development by creating an account on GitHub. Anto, "Tumor detection and classification of MRI brain image using wavelet transform and SVM", 2017 International Conference on Signal Processing and Communic… app. 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. 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. By continuously monitoring users' health data and leveraging predictive models, StorkeScan aims to provide early warnings and educational 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. - ajspurr/stroke_prediction Heart-Disease-Prediction. Find and fix vulnerabilities Aug 28, 2021 · Image from Canva Basic Tooling. This project aims to explore and analyze a dataset related to stroke and build a predictive model to identify potential risk factors. GitHub is where people build software. Data: The data used for this project is a healthcare stroke dataset stored in an AWS S3 bucket. lock files with dependencies for environment; predict. After a stroke, some brain tissues may still be salvageable but we have to move fast. o use SMOTE from . x = df. 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. Feature Selection: The web app allows users to select and analyze specific features from the dataset. This proof-of-concept application is designed for educational purposes and should not be used for medical advice. The app is built using Streamlit, and it predicts the likelihood of a stroke based on real-life data. Initially an EDA has been done to understand the features and later 98% accurate - This stroke risk prediction Machine Learning model utilises ensemble machine learning (Random Forest, Gradient Boosting, XBoost) combined via voting classifier. 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. Flexible Data Ingestion. machine-learning neural-network python3 pytorch kaggle artificial-intelligence artificial-neural-networks tensor kaggle-dataset stroke-prediction Updated Mar 30, 2022 Python Machine Learning Models – Utilize algorithms like Decision Tree and Random Forest for stroke prediction. We intend to implement a prototype that senses relevant parameters and need not necessarily be wearable The dataset used to predict stroke is a dataset from Kaggle. This repository contains code for a brain stroke prediction model built using machine learning techniques. using visualization libraries, ploted various plots like pie chart, count plot, curves Implement an AI system leveraging medical image analysis and predictive modeling to forecast the likelihood of brain strokes. Sign in Product The Stroke Risk Prediction Dataset is a comprehensive dataset designed for machine learning and medical research purposes. Data Classification using python. For example, the KNDHDS dataset has 15,099 total stroke patients, specific regional data, and even has sub classifications for which type of stroke the patient had. GitHub community articles healthcare-dataset-stroke-data. . Contribute to SourcM/health-care-dataset development by creating an account on GitHub. api lists open-source list development public resources dataset free software apis public-api Faker is a Python package The dataset was obtained from Kaggle and contains 5110 rows and 10 columns to detect stroke predictions. As this the machine learning project we will be predicting weather a person having stroke or not using some of the variables in the dataset. csv, which contains medical information such as: 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. Early intervention and preventive measures can be taken to reduce the likelihood of stroke occurrence, potentially saving lives and improving the quality of life for patients. Contribute to Aftabbs/Stroke-Prediction-using-Machine-Learning development by creating an account on GitHub. com. 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). md at main · terickk/stroke-prediction-dataset The Brain Stroke Prediction project has the potential to significantly impact healthcare by aiding medical professionals in identifying individuals at high risk of stroke. Using various data mining techniques, we identify the most important factors for detecting a stroke (i. Data Analysis – Explore and visualize data to understand stroke-related factors. Resources Brain Stroke Prediction is an AI tool using machine learning to predict the likelihood of a person suffering from a stroke by analyzing medical history, lifestyle, and other relevant data. csv │ │ └── stroke_data_final. [ ] Mar 7, 2025 · Dataset Source: Healthcare Dataset Stroke Data from Kaggle. Instant dev environments PREDICTION-STROKE/ ├── data/ │ ├── models/ │ │ ├── best_stroke_model. On this dataset, I have first performed Preprocessing and Visualization, after which I have carried out feature selection. This involves using Python, deep learning frameworks like TensorFlow or PyTorch, and specialized medical imaging datasets for training and validation. Split dataset for training and testing purposes, implemented Ordinal Encoding and One-Hot Encoding to the columns which required. 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 Mar 8, 2024 · Here are three potential future directions for the "Brain Stroke Image Detection" project: Integration with Multi-Modal Data:. healthcare-dataset-stroke-data. 60 % accuracy. Predicting whether a person suffers from stroke using Machine Learning. It includes over 4,000 records and 15 attributes. This project involves the development of a Dockerized RESTful API for predicting stroke occurrence based on a dataset using a Random Forest machine learning model. A Convolutional Neural Network (CNN) is used to perform stroke detection on the CT scan image dataset. Kaggle is an AirBnB for Data Scientists. All 8 Jupyter Notebook 6 Python 2. model --lrsteps 200 250 --epochs 300 --outbasepath ~/tmp/shape --channelscae 1 16 24 32 100 200 1 --validsetsize 0. The steps involve imputing null values, encoding categorical variables, scaling numerical variables, and applying Synthetic Minority Over-sampling Technique (SMOTE) to tackle the issue of unbalanced class data. python api machine-learning google twitter twitter-api depression twitter-sentiment-analysis collaborate googlecolab depression-detection communityexchange This reference kit demonstrates one possible reference implementation of a multi-model and multi-modal solution. Exploratory Data Analysis. 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. - arianarmw/ML01-Stroke-Prediction More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects. ipynb contains the model experiments. The input variables are both numerical and categorical and will be explained below. Stroke Prediction Dataset This repository contains a Stroke Prediction project implemented in Python using machine learning techniques. Framingham Heart study dataset includes several demographic risk factors:-sex: male or female; age: age of the patient Find and fix vulnerabilities Codespaces. app. The dataset used for this analysis can be found in the data directory. The goal of this project is to build a model with an accuracy of 93% to predict stroke. In the code, we have created the instance of the Flask() and loaded the model. model. clm purif pred hodyl bxa rzzyuny zdtedg ghdcicqa seup yjlva kipps nghgliydl trh uuxzqef eosomjz