Kingsley Ukwuoma's Projects
Updated R and Stata and data code from Lopez Bernal IJE 2017
bayesLife that takes into account HIV/AIDS
Experiments to show the usage of deep learning to detect breast cancer from breast histopathology images
Dashboard visualising week-on-week change in coronavirus cases by local authority
Projections of COVID-19, in standardized format
Extensive and accessible COVID-19 data + forecasting at the county-level + hospital-level.
Stata code to produce Demographic and Health Survey Indicators
This project aims to predict the type 2 diabetes, based on the dataset. It uses machine learning model,which is trained to predict the diabetes mellitus before it hits.
Detecting skin cancer in encrypted images with TensorFlow
Heart Disease Analysis repository
Statistical analysis of heart disease data project completed during my enrollment in the Data Science program through Thinkful.
A kaggle competition to predict the likelihood that an HIV patient's infection will become less severe, given a small dataset and limited clinical information.
This repo contains 4 different projects. Built various machine learning models for Kaggle competitions. Also carried out Exploratory Data Analysis, Data Cleaning, Data Visualization, Data Munging, Feature Selection etc
Cleaning and modeling data while investigating the performance capabilities of the Logistic Regression and Xtreme Gradient Boost classification algorithms.
I used six classification techniques, artificial neural network (ANN), Support Vector Machine (SVM), Decision tree (DT), random forest (RF), Logistics Regression (LR) and Naïve Bayes (NB)
A concise and insightful piece on the 'top statistical tools used in medical research and clinical data analysis'. Here, the technical writer focuses on the application thereof; with respect to the field of medical research, systematic reviews, meta-analysis and clinical trials. The objective is to provide exactness in deciding on the appropriate tool for your research problems and unique data.
Process chest x-ray image data, varified and labeled by medical professionals. Using TensorFlow and the Keras API, create and validate convolution neural networks that learn to recognize the presence of pneumonia in the lungs.