Pinned Repositories
arc-development-app
bulldozer-price-prediction
🚜 Predicting the Sale Price of Bulldozers using Machine Learning In this notebook, we're going to go through an example machine learning project with the goal of predicting the sale price of bulldozers.
CIFAR-10-tensorflow
Personal Practice project to evaluate 3 color channel based image in Tensorflow 2.0
dlaicourse
Notebooks for learning deep learning
dog-breed
hacker_news
heart-disease-project
Predicting heart disease using machine learning¶ This notebook looks into using various Python-based machine learning and data science libraries in an attempt to build a machine learning model capable of predicting whether or not someone has heart disease based on their medical attributes. We're going to take the following approach: Problem definition Data Evaluation Features Modelling Experimentation 1. Problem Definition In a statement, Given clinical parameters about a patient, can we predict whether or not they have heart disease? The original data came from the Cleavland data from the UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/heart+Disease There is also a version of it available on Kaggle. https://www.kaggle.com/ronitf/heart-disease-uci 3. Evaluation If we can reach 95% accuracy at predicting whether or not a patient has heart disease during the proof of concept, we'll pursue the project. 4. Features Create data dictionary age - age in years sex - (1 = male; 0 = female) cp - chest pain type 0: Typical angina: chest pain related decrease blood supply to the heart 1: Atypical angina: chest pain not related to heart 2: Non-anginal pain: typically esophageal spasms (non heart related) 3: Asymptomatic: chest pain not showing signs of disease trestbps - resting blood pressure (in mm Hg on admission to the hospital) anything above 130-140 is typically cause for concern chol - serum cholestoral in mg/dl serum = LDL + HDL + .2 * triglycerides above 200 is cause for concern fbs - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) '>126' mg/dL signals diabetes restecg - resting electrocardiographic results 0: Nothing to note 1: ST-T Wave abnormality can range from mild symptoms to severe problems signals non-normal heart beat 2: Possible or definite left ventricular hypertrophy Enlarged heart's main pumping chamber thalach - maximum heart rate achieved exang - exercise induced angina (1 = yes; 0 = no) oldpeak - ST depression induced by exercise relative to rest looks at stress of heart during excercise unhealthy heart will stress more stress more slope - the slope of the peak exercise ST segment 0: Upsloping: better heart rate with excercise (uncommon) 1: Flatsloping: minimal change (typical healthy heart) 2: Downslopins: signs of unhealthy heart ca - number of major vessels (0-3) colored by flourosopy colored vessel means the doctor can see the blood passing through the more blood movement the better (no clots) thal - thalium stress result 1,3: normal 6: fixed defect: used to be defect but ok now 7: reversable defect: no proper blood movement when excercising target - have disease or not (1=yes, 0=no) (= the predicted attribute)
natours-api
Natours API is a RestFul API and a Server side rendered application built with NodeJS and ExpressJS
portfo
profiles-rest-api
Django RestFul API
tanvirakibul's Repositories
tanvirakibul/heart-disease-project
Predicting heart disease using machine learning¶ This notebook looks into using various Python-based machine learning and data science libraries in an attempt to build a machine learning model capable of predicting whether or not someone has heart disease based on their medical attributes. We're going to take the following approach: Problem definition Data Evaluation Features Modelling Experimentation 1. Problem Definition In a statement, Given clinical parameters about a patient, can we predict whether or not they have heart disease? The original data came from the Cleavland data from the UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/heart+Disease There is also a version of it available on Kaggle. https://www.kaggle.com/ronitf/heart-disease-uci 3. Evaluation If we can reach 95% accuracy at predicting whether or not a patient has heart disease during the proof of concept, we'll pursue the project. 4. Features Create data dictionary age - age in years sex - (1 = male; 0 = female) cp - chest pain type 0: Typical angina: chest pain related decrease blood supply to the heart 1: Atypical angina: chest pain not related to heart 2: Non-anginal pain: typically esophageal spasms (non heart related) 3: Asymptomatic: chest pain not showing signs of disease trestbps - resting blood pressure (in mm Hg on admission to the hospital) anything above 130-140 is typically cause for concern chol - serum cholestoral in mg/dl serum = LDL + HDL + .2 * triglycerides above 200 is cause for concern fbs - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) '>126' mg/dL signals diabetes restecg - resting electrocardiographic results 0: Nothing to note 1: ST-T Wave abnormality can range from mild symptoms to severe problems signals non-normal heart beat 2: Possible or definite left ventricular hypertrophy Enlarged heart's main pumping chamber thalach - maximum heart rate achieved exang - exercise induced angina (1 = yes; 0 = no) oldpeak - ST depression induced by exercise relative to rest looks at stress of heart during excercise unhealthy heart will stress more stress more slope - the slope of the peak exercise ST segment 0: Upsloping: better heart rate with excercise (uncommon) 1: Flatsloping: minimal change (typical healthy heart) 2: Downslopins: signs of unhealthy heart ca - number of major vessels (0-3) colored by flourosopy colored vessel means the doctor can see the blood passing through the more blood movement the better (no clots) thal - thalium stress result 1,3: normal 6: fixed defect: used to be defect but ok now 7: reversable defect: no proper blood movement when excercising target - have disease or not (1=yes, 0=no) (= the predicted attribute)
tanvirakibul/arc-development-app
tanvirakibul/CIFAR-10-tensorflow
Personal Practice project to evaluate 3 color channel based image in Tensorflow 2.0
tanvirakibul/dog-breed
tanvirakibul/hacker_news
tanvirakibul/natours-api
Natours API is a RestFul API and a Server side rendered application built with NodeJS and ExpressJS
tanvirakibul/node-farm
A simple NodeJS API
tanvirakibul/portfo
tanvirakibul/robofriends
tanvirakibul/profiles-rest-api
Django RestFul API
tanvirakibul/BI_Course
tanvirakibul/BI_Projects
This repo contains my all business Intelligence Portfolio Projects
tanvirakibul/BI_with_python_course
Business analyst Couse materials & projects
tanvirakibul/Business_Analyt_Course
tanvirakibul/cancer_detection_tensorflow
tanvirakibul/hedge_fund-attack-ml
Predicting Hedge fund attack for Companies
tanvirakibul/house-price-prediction-by-tensorflow
Predicting house prices using Tensorflow
tanvirakibul/learning-tensorflow
tanvirakibul/malaria-cell-detection
Detecting Malaria Cell using Tensorflow 2.0
tanvirakibul/medical_cost_regression
medical cost regression analysis
tanvirakibul/powerbi_course
power bi exercise
tanvirakibul/react-travel-app
tanvirakibul/rnn-on-sine-wave
Generating a singe wave suing RNN.
tanvirakibul/Store_Sales_Time_Series_Forecasting
Use machine learning to predict grocery sales
tanvirakibul/tanvirakibul
tanvirakibul/tanvirakibul.github.io
tanvirakibul/tf_course_filess
tanvirakibul/us-election-stock-market-2020
Time Series Analysis for Stock Frequency in US presidential election 2020
tanvirakibul/weather-prediction-ml
Prediction Weather using Machine Learning
tanvirakibul/ZtM-Job-Board
⚛️ A place for developers to show recruiters they are available for hire