The Trackers

Inspiration

We started our project with the intent to help prevent the spread of the Coronavirus

What it does

Predicts the path of a healthcare outbreak. We have special; transformation tools that can take WHO/Open source data and plot the spread. We can predict how far it can go. It may not be very accurate. We have universal api which can be used by anyone who wants to share their data. All covid Survivors are Welcome.

How we built it

python, tableau, r studio ,Aws kinesis,

Challenges we ran into

While cleaning the data set we ran into computing errors. We fixed them by editing the data set and clearing NANs.

Accomplishments that we're proud of

A working model

What we learned

How to create plots and predictive analysis that can hopefully be applied to future outbreaks

What's next for The Trackers

Applying this tool to track the next pandemics with the intent of preventing fake news. We hope to customize our tool to the next outbreak in order to save at risk groups.

our sample code

title: "SamplePredict" author: "Sneha Gondane" date: "2/29/2020" output: html_document: default pdf_document: default

word_document: default

require(tidyverse)
data <- read_csv('C://Users/Rahul/Documents/SunFlowerHack2020/sample3.csv')
data
require(lattice)

install.packages('leaps')
require(leaps)
lps=regsubsets(type~Lat+Long+cases
,data=data, method='backward')
plot(lps,scale='adjr2')

#To reproducd data from a consistent starting point using seed value
require(tidyverse)
set.seed(1234)
#Select training data (using 60 / 40 ratio)
training.data=data %>% sample_frac(size=.6)
#training.data
#Select testing data
testing.data=data %>% anti_join(training.data,by='ID')
#testing.data
training.input=training.data%>%select(-type, -ID)
#training.input
testing.input=testing.data %>% select(-type, -ID)
#testing.input
training.label=training.data$type
#training.label
testing.label=testing.data$type
#testing.label

require(class)
predictions=knn(train=training.input, test=testing.input, cl=training.label, k=25)
data.frame(predictions,testing.label)
summary(data.frame(predictions,testing.label))
accuracy = sum(predictions==testing.label) / length(predictions)
accuracy