Data is provided by IHME through their GBD Results Tool. The data consists of 29 cancer types broken down by three measures (Incidence, Prevalence, Deaths), from the years 1990-2016. In : # setup environment import pandas as pd import matplotlib.pyplot as plt import seaborn as sns %matplotlib inline Data In : # read in data cancer = pd.read_csv('IHME-GBD_2016_DATA-b922583c-1.csv') In : … Continue reading How are Cancer Rates Trending? 1990-2016
The following data visualization was developed in Microsoft Power BI. It allows you to explore cancer rates by cancer type, sex, and US state from various metric perspectives: prevalence, death, DALYs (Disability-adjusted life years), and YLLs (Years of life lost). The data is compiled by IHME (Institute for Health Metrics & Evaluation) and can be … Continue reading What Are the Cancer Rates of Various Metrics for Each US State in 2016?
This dataset is part of the Scikit-learn dataset package. It is from the Breast Cancer Wisconsin (Diagnostic) Database and contains 569 instances of tumors that are identified as either benign (357 instances) or malignant (212 instances). This machine learning project seeks to predict the classification of breast tumors as either malignant or benign. More information … Continue reading Predicting Breast Cancer Using Logistic Regression