About the data

The data is collected from cancer.gov and the US Census American Community Survey. There are 34 columns including the target column. Some of the columns are listed below:

TARGET_deathRate: Dependent variable. Mean per capita (100,000) cancer mortalities(a)
avgAnnCount: Mean number of reported cases of cancer diagnosed annually(a)
avgDeathsPerYear: Mean number of reported mortalities due to cancer(a)
incidenceRate: Mean per capita (100,000) cancer diagoses(a)
medianIncome: Median income per county (b)
popEst2015: Population of county (b)
povertyPercent: Percent of populace in poverty (b)
studyPerCap: Per capita number of cancer-related clinical trials per county (a)
binnedInc: Median income per capita binned by decile (b)
MedianAge: Median age of county residents (b)
MedianAgeMale: Median age of male county residents (b)
MedianAgeFemale: Median age of female county residents (b)
Geography: County name (b)
AvgHouseholdSize: Mean household size of county (b)
PercentMarried: Percent of county residents who are married (b)
PctNoHS18_24: Percent of county residents ages 18-24 highest education attained: less than high school (b)
PctHS18_24: Percent of county residents ages 18-24 highest education attained: high school diploma (b)
PctSomeCol18_24: Percent of county residents ages 18-24 highest education attained: some college (b)
PctBachDeg18_24: Percent of county residents ages 18-24 highest education attained: bachelor's degree (b)
PctHS25_Over: Percent of county residents ages 25 and over highest education attained: high school diploma (b)
PctBachDeg25_Over: Percent of county residents ages 25 and over highest education attained: bachelor's degree (b)
PctEmployed16_Over: Percent of county residents ages 16 and over employed (b)
PctUnemployed16_Over: Percent of county residents ages 16 and over unemployed (b)
PctPrivateCoverage: Percent of county residents with private health coverage (b)
PctPrivateCoverageAlone: Percent of county residents with private health coverage alone (no public assistance) (b)
PctEmpPrivCoverage: Percent of county residents with employee-provided private health coverage (b)
PctPublicCoverage: Percent of county residents with government-provided health coverage (b)
PctPubliceCoverageAlone: Percent of county residents with government-provided health coverage alone (b)
PctWhite: Percent of county residents who identify as White (b)
PctBlack: Percent of county residents who identify as Black (b)
PctAsian: Percent of county residents who identify as Asian (b)
PctOtherRace: Percent of county residents who identify in a category which is not White, Black, or Asian (b)
PctMarriedHouseholds: Percent of married households (b)
BirthRate: Number of live births relative to number of women in county (b)

(a): years 2010-2016

(b): 2013 Census Estimates

To load the training data in your jupyter notebook, use the below command:

import pandas as pd

cancer_data = pd.read_csv("https://raw.githubusercontent.com/dphi-official/Datasets/master/cancer_death_rate/Training_set_label.csv" )

Saving Prediction File & Sample Submission

You can find more details on how to save a prediction file here: https://discuss.dphi.tech/t/how-to-submit-predictions/548

Sample submission: You should submit a CSV file with a header row and the sample submission can be found below.

prediction

164.9

161.3

174.7

194.8

144.4

176

.

.

Etc.

Note that the header name should be prediction else it will throw an evaluation error. A sample submission file can be found here

Test Dataset

Load the test data (name it as test_data). You can load the data using the below command.

test_data = pd.read_csv('https://raw.githubusercontent.com/dphi-official/Datasets/master/cancer_death_rate/Testing_set_label.csv')

Here the target column is deliberately not there as you need to predict it

Cancer Death Rate

Challenge Starts

Challenge Ends