by towi_parallelism
Last Updated September 18, 2018 14:19 PM

I am trying to build a model in R to predict Conversion Rate (CR) based on age, gender, and interest (and also the campaign_Id):

The CR values look like this:

The correlation coefficients are not very promising:

`rcorr(as.matrix(data.numeric))`

correlations with CR:

xyz_campaign_id (-0.19), age (-0.1), gender(-0.04), interest(-0.03)

So, the model below:

```
library(caret)
set.seed(100)
TrainIndex <- sample(1:nrow(data), 0.8*nrow(data))
data.train <- data[TrainIndex,]
data.test <- data[-TrainIndex,]
nrow(data.test)
model <- lm(CR ~ age + gender + interest + xyz_campaign_id , data=data.train)
```

will not have a good adjusted r-squared (0.04):

```
Call:
lm(formula = CR ~ age + gender + interest + xyz_campaign_id,
data = data.train)
Residuals:
Min 1Q Median 3Q Max
-18.636 -11.858 -4.087 0.115 96.421
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 47.231250 6.287738 7.512 1.4e-13 ***
age35-39 1.214713 1.916649 0.634 0.52639
age40-44 -1.971037 1.986316 -0.992 0.32131
age45-49 -3.064858 1.866713 -1.642 0.10097
genderM 3.709192 1.412311 2.626 0.00878 **
interest 0.030384 0.027617 1.100 0.27154
xyz_campaign_id -0.037856 0.006076 -6.231 7.1e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 21.16 on 907 degrees of freedom
Multiple R-squared: 0.05237, Adjusted R-squared: 0.04611
F-statistic: 8.355 on 6 and 907 DF, p-value: 7.81e-09
```

I also understand that I should probably convert "interest" from numeric to factor (I have tried that too, although I considered all 40 interest levels which is not ideal)

So, based on the provided information, is there any way to improve the model? what other models shall I try besides linear models to make sure that I have a good predictive model?

If you need more information, the challenge is available Here

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