MD RAKIBUL HASSAN's Projects
100+ python programming exercise problem discussed ,explained and solved in different ways
This repo contains my all business Intelligence Portfolio Projects
Business analyst Couse materials & projects
π 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.
Personal Practice project to evaluate 3 color channel based image in Tensorflow 2.0
FREE Bootstrap 4 Portfolio Theme for Developers -
Notebooks for learning deep learning
Predicting heart disease using machine learningΒΆ 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)
Predicting Hedge fund attack for Companies
Predicting house prices using Tensorflow
Mobile App Development Contest Submission
Atom linter plugin for JavaScript, using JavaScript Standard Style
Detecting Malaria Cell using Tensorflow 2.0
medical cost regression analysis
Natours API is a RestFul API and a Server side rendered application built with NodeJS and ExpressJS
A simple NodeJS API
power bi exercise
Django RestFul API