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Startup.R
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# load required libraries
library(beakr)
library(mlr)
#load trained model named 'rforest' from the current working directory
load("RandomForest.RData")
# function that combines the observations in a
# dataframe, calculates a prediction with the previously loaded model and returns
# the prediction
# Requirements:
# - Variables of the observed startup (no missing data is allowed)
#
# Output:
# - Result of the prediction returned in a string
get_prediction = function(longitude, latitude, affy, alfy, afmy, almy, relationships,
funding_rounds, funding_total_usd, milestones, is_CA, is_NY,
is_MA, is_otherstate, is_software, is_web, is_mobile,
is_enterprise, is_advertising, is_othercategory, has_VC,
has_angel, has_roundA, has_roundB, has_roundC, has_roundD,
is_top500){
# combine parameters in data.frame
startup_obs = data.frame(
# latitude of the startup
latitude = as.numeric(as.vector(latitude)),
# longitude of the startup
longitude = as.numeric(as.vector(longitude)),
# age in years at the first funding round
age_first_funding_year = as.numeric(as.vector(affy)),
longitude = as.numeric(as.vector(longitude)),
# age in years at the last funding round
age_last_funding_year = as.numeric(as.vector(alfy)),
longitude = as.numeric(as.vector(longitude)),
# age in years at reaching the first milestone
age_first_milestone_year = as.numeric(as.vector(afmy)),
longitude = as.numeric(as.vector(longitude)),
# age in years at reaching the last milestone
age_last_milestone_year = as.numeric(as.vector(almy)),
# number of relationships (ex. accountants, vendors, investors, mentors,...)
relationships = as.integer(as.vector(relationships)),
# number of executed funding rounds
funding_rounds = as.integer(as.vector(funding_rounds)),
# overall raised money during funding rounds
funding_total_usd = as.numeric(as.vector(funding_total_usd)),
# number of reached milestones
milestones = as.integer(as.vector(milestones)),
# 1 if startup is located in California, else 0
is_CA = factor(is_CA, levels = c(0, 1)),
# 1 if startup is located in New York, else 0
is_NY = factor(is_NY, levels = c(0, 1)),
# 1 if startup is located in Massachusetts, else 0
is_MA = factor(is_MA, levels = c(0, 1)),
# 1 if startup is located in another state, else 0
is_otherstate = factor(is_otherstate, levels = c(0, 1)),
# 1 if startup is working in the software industry, else 0
is_software = factor(is_software, levels = c(0, 1)),
# 1 if startup is working in the web industry, else 0
is_web = factor(is_web, levels = c(0, 1)),
# 1 if startup is working in the mobile industry, else 0
is_mobile = factor(is_mobile, levels = c(0, 1)),
# 1 if startup is working in the enterprise industry, else 0
is_enterprise = factor(is_enterprise, levels = c(0, 1)),
# 1 if startup is working in the advertising industry, else 0
is_advertising = factor(is_advertising, levels = c(0, 1)),
# 1 if startup is working in another industry, else 0
is_othercategory = factor(is_othercategory, levels = c(0, 1)),
# 1 if startup has raised venture capital, else 0
has_VC = factor(has_VC, levels = c(0, 1)),
# 1 if startup has an business angel, else 0
has_angel = factor(has_angel, levels = c(0, 1)),
# 1 if startup conducted 1 funding round, else 0
has_roundA = factor(has_roundA, levels = c(0, 1)),
# 1 if startup conducted 2 funding rounds, else 0
has_roundB = factor(has_roundB, levels = c(0, 1)),
# 1 if startup conducted 3 funding rounds, else 0
has_roundC = factor(has_roundC, levels = c(0, 1)),
# 1 if startup conducted 4 funding rounds, else 0
has_roundD = factor(has_roundD, levels = c(0, 1)),
# 1 if startup was ranked among the top 500, else 0
is_top500 = factor(is_top500, levels = c(0, 1)))
# calculate prediction for the observed startup
pred = predict(rforest, newdata = startup_obs)
#return the prediction
return((paste0("The prediction for the observed startup is: ",
as.numeric(as.vector(pred$data$response)), "\n", "With a probability of ",
100 * pred$data$prob.1, "% for a prediction of a successful startup (Threshold is 50%)")))
}
# generate new beakr server
newBeakr() %>%
# Respond to GET requests at the "/predict" route
httpGET(path = "/predict", decorate(get_prediction)) %>%
# Handle any errors with a JSON response
handleErrors() %>%
# Start the server on port 8001
listen(host = "0.0.0.0", port = 8001)