Stroke-predictions-ml-model - Machine learning model to predict individuals chances of having a stroke

Related tags

Deep Learning machine-learning strokes stroke-prediction
Overview

stroke-predictions-ml-model

machine learning model to predict individuals chances of having a stroke link to heroku app: https://ml-stroke-predictions.herokuapp.com/

here you will find all code needed to run stroke predictions. in the ipynb notebooks are the model and the exploratory analysis on my dataset used to make decisions on my model.

In the templates folder are the html files needed to run the app

In the app.py folder is the flask code used to connect the front end with the back end and display a prediction based on the model.

You might also like...
Implementation of the bachelor's thesis "Real-time stock predictions with deep learning and news scraping".

Real-time stock predictions with deep learning and news scraping This repository contains a partial implementation of my bachelor's thesis "Real-time

David Álvarez de la Torre 0 Feb 9, 2022
Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.
Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.

Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.

Nerdy Rodent 2.3k Jan 4, 2023
Just playing with getting CLIP Guided Diffusion running locally, rather than having to use colab.
Just playing with getting CLIP Guided Diffusion running locally, rather than having to use colab.

CLIP-Guided-Diffusion Just playing with getting CLIP Guided Diffusion running locally, rather than having to use colab. Original colab notebooks by Ka

Nerdy Rodent 336 Dec 9, 2022
In the case of your data having only 1 channel while want to use timm models

timm_custom Description In the case of your data having only 1 channel while want to use timm models (with or without pretrained weights), run the fol

null 2 Nov 26, 2021
An end-to-end machine learning web app to predict rugby scores (Pandas, SQLite, Keras, Flask, Docker)
An end-to-end machine learning web app to predict rugby scores (Pandas, SQLite, Keras, Flask, Docker)

Rugby score prediction An end-to-end machine learning web app to predict rugby scores Overview An demo project to provide a high-level overview of the

null 34 May 24, 2022
Using machine learning to predict and analyze high and low reader engagement for New York Times articles posted to Facebook.
Using machine learning to predict and analyze high and low reader engagement for New York Times articles posted to Facebook.

How The New York Times can increase Engagement on Facebook Using machine learning to understand characteristics of news content that garners "high" Fa

Jessica Miles 0 Sep 16, 2021
Using python and scikit-learn to make stock predictions

MachineLearningStocks in python: a starter project and guide EDIT as of Feb 2021: MachineLearningStocks is no longer actively maintained MachineLearni

Robert Martin 1.3k Dec 29, 2022
Systematic generalisation with group invariant predictions

Requirements are Python 3, TensorFlow v1.14, Numpy, Scipy, Scikit-Learn, Matplotlib, Pillow, Scikit-Image, h5py, tqdm. Experiments were run on V100 GPUs (16 and 32GB).

Faruk Ahmed 30 Dec 1, 2022
Neural Ensemble Search for Performant and Calibrated Predictions
Neural Ensemble Search for Performant and Calibrated Predictions

Neural Ensemble Search Introduction This repo contains the code accompanying the paper: Neural Ensemble Search for Performant and Calibrated Predictio

AutoML-Freiburg-Hannover 26 Dec 12, 2022
Owner
Alex Volchek
Current student studying Computer Science at YU in NYC. Really passionate about startups and learning about the entrepreneurial process!
Alex Volchek
GitHub
ReConsider is a re-ranking model that re-ranks the top-K (passage, answer-span) predictions of an Open-Domain QA Model like DPR (Karpukhin et al., 2020).

ReConsider ReConsider is a re-ranking model that re-ranks the top-K (passage, answer-span) predictions of an Open-Domain QA Model like DPR (Karpukhin

Facebook Research 47 Jul 26, 2022
Patient-Survival - Using Python, I developed a Machine Learning model using classification techniques such as Random Forest and SVM classifiers to predict a patient's survival status that have undergone breast cancer surgery.

Patient-Survival - Using Python, I developed a Machine Learning model using classification techniques such as Random Forest and SVM classifiers to predict a patient's survival status that have undergone breast cancer surgery.

Nafis Ahmed 1 Dec 28, 2021
Diabetes-Feature-Engineering - A machine learning model that can predict whether people have diabetes when their characteristics are specified

Diabetes-Feature-Engineering Aim Developing a machine learning model that can pr

Özlem Taşkın 0 Feb 23, 2022
Predict stock movement with Machine Learning and Deep Learning algorithms

Project Overview Stock market movement prediction using LSTM Deep Neural Networks and machine learning algorithms Software and Library Requirements Th

Naz Delam 46 Sep 13, 2022
The official code of Anisotropic Stroke Control for Multiple Artists Style Transfer

ASMA-GAN Anisotropic Stroke Control for Multiple Artists Style Transfer Proceedings of the 28th ACM International Conference on Multimedia The officia

Six_God 146 Nov 21, 2022
Joint parameterization and fitting of stroke clusters

StrokeStrip: Joint Parameterization and Fitting of Stroke Clusters Dave Pagurek van Mossel1, Chenxi Liu1, Nicholas Vining1,2, Mikhail Bessmeltsev3, Al

Dave Pagurek 44 Dec 1, 2022
Homepage of paper: Paint Transformer: Feed Forward Neural Painting with Stroke Prediction, ICCV 2021.

Paint Transformer: Feed Forward Neural Painting with Stroke Prediction [Paper] [PaddlePaddle Implementation] Homepage of paper: Paint Transformer: Fee

null 442 Dec 16, 2022
ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles for Stroke Forecasting in Badminton (AAAI 2022)

ShuttleNet: Position-aware Rally Progress and Player Styles Fusion for Stroke Forecasting in Badminton (AAAI 2022) Official code of the paper ShuttleN

Wei-Yao Wang 11 Nov 30, 2022
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

H2O H2O is an in-memory platform for distributed, scalable machine learning. H2O uses familiar interfaces like R, Python, Scala, Java, JSON and the Fl

H2O.ai 6.1k Jan 5, 2023
naked is a Python tool which allows you to strip a model and only keep what matters for making predictions.

naked is a Python tool which allows you to strip a model and only keep what matters for making predictions. The result is a pure Python function with no third-party dependencies that you can simply copy/paste wherever you wish.

Max Halford 24 Dec 20, 2022