Preparing Training and Test Data

We see that InitialDonation, LastDonation and Lifetimesum are factos .. so let´s prepare the data.

lifetimedata$InitialDonation <- as.numeric(lifetimedata$InitialDonation) ## InitialDonation to numeric
lifetimedata$LastDonation <- as.numeric(lifetimedata$LastDonation)  ## LastDonation to numeric
lifetimedata$Age <- as.numeric(lifetimedata$Age) ## Age to numeric
lifetimedata$Lifetimesum <- as.numeric(lifetimedata$Lifetimesum) ## Lifetimesum to numeric
str(lifetimedata) ## another look at the file

## 'data.frame':    4000 obs. of  5 variables:
##  $ DonorId        : int  166 1749 2500 1024 858 2130 446 2571 2119 139 ...
##  $ InitialDonation: num  2209 2557 2110 1352 2044 ...
##  $ LastDonation   : num  3260 3038 2854 2794 2758 ...
##  $ Age            : num  18 18 18 18 18 18 18 18 18 18 ...
##  $ Lifetimesum    : num  2450 2404 1881 1663 1 ...

lifetimedata <- lifetimedata[,2:5] ## DonorId is not needed ... we subset columns 2 to 6
str(lifetimedata) ## check whether subset worked

## 'data.frame':    4000 obs. of  4 variables:
##  $ InitialDonation: num  2209 2557 2110 1352 2044 ...
##  $ LastDonation   : num  3260 3038 2854 2794 2758 ...
##  $ Age            : num  18 18 18 18 18 18 18 18 18 18 ...
##  $ Lifetimesum    : num  2450 2404 1881 1663 1 ...

As we have a decent dataset now we go ahead and load the promiment machine learning package Caret (Classification And Regression Training)

Data cleaning

So called near-zero-variance variables (i.e. variables where the observations are all the same) are cleaned.

library(caret) ## we load the Caret Package"

## Warning: package 'caret' was built under R version 3.3.3

## Loading required package: lattice

## Loading required package: ggplot2

inTrain <- createDataPartition(y=lifetimedata$Lifetimesum, p=0.75, list=FALSE) ## createDataPartition splits in train and test set
training <- lifetimedata[inTrain,]
testing <- lifetimedata[-inTrain,]
dim(training) ## the training set contains 3.000 cases

## [1] 3000    4

Intercorrelation

Before we fit the model, let´s have a look at intercorrelations

library(corrplot) # we load the library corrplot

## Warning: package 'corrplot' was built under R version 3.3.3

corrplotbase <- cor(training)
corrplot(corrplotbase, method="pie") ## apply the corrpllot with method "pie"

Fitting the model

Now it is the moment we fit the (regression) model:

## now we fit the linear model from Caret
lmfit <- train(Lifetimesum ~ .,
               data = training, 
               method = "lm")
summary(lmfit)

## 
## Call:
## lm(formula = .outcome ~ ., data = dat)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2100.18 -1003.85     3.03   972.65  2093.47 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1856.10651   75.92703  24.446  < 2e-16 ***
## InitialDonation   -0.01945    0.02652  -0.733    0.463    
## LastDonation       0.09741    0.02213   4.403 1.11e-05 ***
## Age                0.01976    0.99975   0.020    0.984    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1145 on 2996 degrees of freedom
## Multiple R-squared:  0.006653,   Adjusted R-squared:  0.005658 
## F-statistic: 6.688 on 3 and 2996 DF,  p-value: 0.0001696

finalmodel <- lmfit$finalModel
testing$pred<- predict(finalmodel, testing) ## we add the prediction column to the test dataset
require("lattice")
xyplot(testing$Lifetimesum ~ testing$pred, data = testing, auto.key = TRUE, pch = 20, col = 'red')

The scatterplot illustrates the relative poorness of the prediction. So: Still some work (data collection, modelling) to be done :-)