Built various Machine Learning algorithms (Logistic Regression, Random Forest, KNN, Gradient Boosting and XGBoost. etc)

Overview

Heart-Failure-Prediction

Built various Machine Learning algorithms (Logistic Regression, Random Forest, KNN, Gradient Boosting and XGBoost. etc). Structured a custom ensemble model and a neural network. Found a outperformed model for heart failure prediction accuracy of 88 percent.

Introduction

Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide. Four out of 5CVD deaths are due to heart attacks and strokes. Heart failure is a common event caused by CVDs and this dataset contains 11 features that can be used to predict a possible heart disease.

People with cardiovascular disease or who are at high cardiovascular risk need early detection and management wherein a machine learning model can be of great help.

Table of Contents

  • [Data]

    • [What We need to do]
  • [Exploratory Data Analysis]

    • [Target Variable]
    • [Features]
  • [Model Selection]

    • [Model Creation and Comparison]
    • [Bulid a custom ensemble (superlearner) with best three of models]
    • [Neural Networks]
    • [Feature Importance]
  • [Conclusion]

DATA

1 Age: Age of the patient [years]

2 Sex: Sex of the patient [M: Male, F: Female]

3 ChestPainType: [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic]

4 RestingBP: Resting blood pressure [mm Hg]

5 Cholesterol: Serum cholesterol [mm/dl]

6 FastingBS: Fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise]

7 RestingECG: Resting electrocardiogram results [Normal: Normal, ST: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV), LVH: showing probable or definite left ventricular hypertrophy by Estes' criteria]

8 MaxHR: Maximum heart rate achieved [Numeric value between 60 and 202]

9 ExerciseAngina: Exercise-induced angina [Y: Yes, N: No]

10 Oldpeak: ST [Numeric value measured in depression] (

11 ST_Slope: The slope of the peak exercise ST segment [Up: upsloping, Flat: flat, Down: downsloping]

12 HeartDisease: Output class [1: heart disease, 0: Normal]

Reference: https://www.kaggle.com/fedesoriano/heart-failure-prediction

Owner
Chris Yuan
Motivated, open-minded, and detail-oriented student currently working towards a degree in Data Science.
Chris Yuan
Mosec is a high-performance and flexible model serving framework for building ML model-enabled backend and microservices

Mosec is a high-performance and flexible model serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and t

164 Jan 04, 2023
YouTube Spam Detection with python

YouTube Spam Detection This code deletes spam comment on youtube videos based on two characteristics (currently) If the author of the comment has a se

MohamadReza Taalebi 5 Sep 27, 2022
A repository for collating all the resources such as articles, blogs, papers, and books related to Bayesian Statistics.

A repository for collating all the resources such as articles, blogs, papers, and books related to Bayesian Statistics.

Aayush Malik 80 Dec 12, 2022
whylogs: A Data and Machine Learning Logging Standard

whylogs: A Data and Machine Learning Logging Standard whylogs is an open source standard for data and ML logging whylogs logging agent is the easiest

WhyLabs 2k Jan 06, 2023
A game theoretic approach to explain the output of any machine learning model.

SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allo

Scott Lundberg 18.2k Jan 02, 2023
Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Now updated with Dask to handle millions of rows.

Auto_TS: Auto_TimeSeries Automatically build multiple Time Series models using a Single Line of Code. Now updated with Dask. Auto_timeseries is a comp

AutoViz and Auto_ViML 519 Jan 03, 2023
Predict the demand for electricity (R) - FRENCH

06.demand-electricity Predict the demand for electricity (R) - FRENCH Prédisez la demande en électricité Prérequis Pour effectuer ce projet, vous devr

1 Feb 13, 2022
A benchmark of data-centric tasks from across the machine learning lifecycle.

A benchmark of data-centric tasks from across the machine learning lifecycle.

61 Dec 28, 2022
Simplify stop motion animation with machine learning.

Simplify stop motion animation with machine learning.

Nick Bild 25 Sep 15, 2022
Anomaly Detection and Correlation library

luminol Overview Luminol is a light weight python library for time series data analysis. The two major functionalities it supports are anomaly detecti

LinkedIn 1.1k Jan 01, 2023
A model to predict steering torque fully end-to-end

torque_model The torque model is a spiritual successor to op-smart-torque, which was a project to train a neural network to control a car's steering f

Shane Smiskol 4 Jun 03, 2022
Bayesian Additive Regression Trees For Python

BartPy Introduction BartPy is a pure python implementation of the Bayesian additive regressions trees model of Chipman et al [1]. Reasons to use BART

187 Dec 16, 2022
Practical Time-Series Analysis, published by Packt

Practical Time-Series Analysis This is the code repository for Practical Time-Series Analysis, published by Packt. It contains all the supporting proj

Packt 325 Dec 23, 2022
A Python implementation of FastDTW

fastdtw Python implementation of FastDTW [1], which is an approximate Dynamic Time Warping (DTW) algorithm that provides optimal or near-optimal align

tanitter 651 Jan 04, 2023
Machine learning algorithms implementation

Machine learning algorithms implementation This repository consisits of implementation of various machine learning algorithms. The algorithms implemen

Karun Dawadi 1 Jan 03, 2022
Conducted ANOVA and Logistic regression analysis using matplot library to visualize the result.

Intro-to-Data-Science Conducted ANOVA and Logistic regression analysis. Project ANOVA The main aim of this project is to perform One-Way ANOVA analysi

Chris Yuan 1 Feb 06, 2022
ML Optimizers from scratch using JAX

Toy implementations of some popular ML optimizers using Python/JAX

Shreyansh Singh 38 Jul 29, 2022
BASTA: The BAyesian STellar Algorithm

BASTA: BAyesian STellar Algorithm Current stable version: v1.0 Important note: BASTA is developed for Python 3.8, but Python 3.7 should work as well.

BASTA team 16 Nov 15, 2022
A quick reference guide to the most commonly used patterns and functions in PySpark SQL

Using PySpark we can process data from Hadoop HDFS, AWS S3, and many file systems. PySpark also is used to process real-time data using Streaming and

Sundar Ramamurthy 53 Dec 21, 2022
pywFM is a Python wrapper for Steffen Rendle's factorization machines library libFM

pywFM pywFM is a Python wrapper for Steffen Rendle's libFM. libFM is a Factorization Machine library: Factorization machines (FM) are a generic approa

João Ferreira Loff 251 Sep 23, 2022