The document describes predictive models created to predict charitable donors and donation amounts. Logistic regression and support vector machines (SVM) were the best performing models. The models can predict likely donors with 58.7% accuracy and estimate donation amounts, achieving lifts of 1.2-2.3x over no model. The most influential predictors of donations were a donor's giving frequency and last donation amount. Validating on new data, the models were estimated to generate $12,339 in additional donations.

1. 1
Predicting Likely Donors
and
Donation Amounts
Predictive Analytics
Michele Vincent
March 22, 2017

2. 2
Goals
• Predict likely donors using classification models
• Predict how much donation will likely donors give using regression
models
• Validate predictive models by measuring how effective the models
are
Objectives

3. 3
Training Data
• Filename: cup98lrn variable
subset small.txt
• # Records: 50% of 95,412 (47,706)
• Target Variable: TARGET_B
– Classification (Y/N decision, a
donor or not)
– 5% responders
Testing Data
• Filename: cup98lrn variable
subset small.txt
• # Records: 50% of 95,412 (47,706)
• Target Variable: TARGET_B
– Classification (Y/N decision, a
donor or not)
– 5% responders
Data Used for Training and
Testing of Classification Models
TARGET_B is binary indicator for response to 97NK Mailing

4. 4
Validation Data
• Filename: cup98val_variable_subset_small.csv
• # Records: 96,367
• Target Variable: TARGET_B
– Classification (Y/N decision, a donor or not)
– 5% responders
Data Used for Validation of Classification Models
TARGET_B is binary indicator for response to 97NK Mailing

5. 55 5
 Based on AUC, the best model is the Logistic Regression model which generates the highest
AUC. It correctly classified 58.7% which is not the highest but it is one of the highest. It’s
precision is 2nd highest. The lift it provides at 10% or 70% is one of the highest among all
models tested.
Classification Model: Accuracy
• Best Performing Algorithm
Stratified and equal size sampling were used for all models tested below.

6. 66 6
 The accuracy rate of the Logistic Regression is 58.7%. This means we correctly classified 58.7%
of the file. If we had 100 records, there were 58 that we correctly classified as non-donors and
donors.
 The precision means how many donors did we get right out of the total that we predicted as
donors. If 100 donors were predicted, but only 7 of them are actual donors, then the precision
is 7%.
 The recall means how many donors did we get right out of the total actual donors. If there were
100 actual donors, and we predicted 58 of them correctly, then the recall rate is 58%.
Sensitivity is also recall.
 False alarm means we thought a person is a donor, but he wasn’t. If there were 100 non-donors
and we claimed 41 of them to be donors, then our false alarm rate is 41%. In this example, we
would claim that 59 are not donors. This means that the Specificity is 59%.
Classification Model: Accuracy (Cont’d)
• Logistic Regression

7. 77 7
Classification Model: Accuracy (Cont’d)
• ROC Curve for the winning model (Logistic Regression):
 ROC curve shows an area under the curve of
0.6110 (which is the biggest area under the curve
among other models).
 This curve is also useful for knowing what true
alarm rate we can get given an accepted false
alarm rate.
 If we are willing to accept a 37% false alarm rate
we can get a true alarm rate of 55% (dotted line
on the graph). This means that if we are lenient
and allow the model to make a mistake of
classifying 37 donors when they are not actual
donors, then the model can get us 55 donors who
are actual donors.
False Alarm
TrueAlarm

8. 88 8
Classification Model: Accuracy (Cont’d)
• Lift Chart for the winning model (Logistic Regression)
 The lift at 10% of the file is 1.815; at
70%, the cumulative lift is 1.12.
 This means that if we mailed to the top
10% of the file which contains the
predictions with highest probabilities,
we can get 1.8 more donors than just
1.0 if we do not use a model.
y-axis shows 1.815 (lift) and x-axis shows 10 (percentage of file).

9. 99 9
Classification Model: Accuracy (Cont’d)
• Histogram for the winning model (Logistic Regression)
 The histogram of the distribution of
predictions shows:
• 921 are predicted with probability to respond between 0.507
and 0.608
• 429 are predicted with probability to respond between 0.608
and 0.709
 This means that if we are comfortable
mailing only to those with probability
greater than 0.6, then we can expect at least
429 donors with that probability to respond.
429
921
748
305

10. 101010
 We validated the results of our best model using a different set of data. The results here are
very close to results previously discussed.
Classification Model: Accuracy (Cont’d)
• Classification Accuracy
Using Validation Data

11. 111111
Classification Model: Interpretation
• Best Predictors
 RFA_2F* was chosen by five models to be their top predictor for a donor.
If we were to differentiate between a non-donor and a donor, RFA_2F is
the best variable to use.
 E_RFA_2A was chosen by three models to be one of their top three
predictors.
 FISTDATE and NGIFTALL were chosen by two models to be one of their
top three predictors.
* Frequency code for donor’s RFA status as of 97NK promotion date

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Classification Model: Interpretation (Cont’d)
• Details of Best Predictors for Some of the Models Used:
 Three common predictors appeared among the top predictors from
Logistic Regression, Neural Networks and kNN = 101 models:
 RFA_2F
 E_RFA_2A
 FISTDATE

13. 131313
Classification Model: Interpretation (Cont’d)
• Relationship of Predictors to Target Variable:
 LASTGIFT’s P-value is not significant, and therefore, not
an influential variable. It does not matter how much a
donor gave last time. The amount the donor gave does not
help in predicting whether he will be a donor again.
 FISTDATE and DOMAIN3 have negative relationship with
the target variable. The smaller (or less recent) the
FISTDATE is, the more likely they are to be a responder.
The likely donor is someone who has not given recently,
and is not from the lowest socio-economic status.
 RFA_2F, D_RFA_2A, E_RFA_2A, DOMAIN1 have
positive relationship with the target variable. The bigger
these variables are, the more likely that the outcome of
Donor=Y is true. The likely donor is someone who is a
frequent giver, and comes from the highest socio-economic
status.
 The D_RFA_2A has higher coefficient than other
predictors which means D_RFA_2A has larger influence
on our prediction that someone is a donor than other
predictors. So, the donor’s RFA status as of the 97K
promotion is more influential than his RFA status as of the
96NK or 95NK promotions.
Donor=Y

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Classification Model: Conclusion
• The donor’s frequency of giving is the most influential variable to
determine a donor and a non-donor.
• The likely donor is someone who has not given recently but is a frequent
giver, and comes from the highest socio-economic status.
• If the results of the Logistic Regression model is implemented in the next
campaign, and we know the model gives a 1.2 cumulative lift at 70% of the
file, we can expect to gain out of 70,000 mailings 4,200 responders. We’ll
get a higher response rate of 6% instead of 5% without a model, and we’ll
save some money from the cost of 30,000 mailings.

15. 15
Training Data
• Filename: cup98lrn variable
subset small responders.txt
• # Records: 50% of 4,873
• Target Variable: TARGET_D
– Average Continuous Value
(donation amount)
– 5% responders
Testing Data
• Filename: cup98lrn variable
subset small responders.txt
• # Records: 50% of 4,873
• Target Variable: TARGET_D
– Average Continuous Value
(donation amount)
– 5% responders
Data Used for Training and Testing of
Regression Models
TARGET_D is donation amount associated with
the response to 97NK Mailing

16. 16
Validation Data
• Filename: cup98val_variable_subset_small.csv
• # Records: 96,367
• Target Variable: TARGET_D
– Average Continuous Value (donation amount)
– 5% responders
Data Used for Validation of Regression Models
TARGET_D is donation amount associated with
the response to 97NK Mailing

17. 171717
Regression Model: Accuracy
 Using original target variable, SVM wins. The model SVM is the best model having the highest
R-squared value. SVM also has the smallest mean absolute error, mean squared error and root
mean squared deviation than Linear Regression and Neural Net.
 Using transformed target variable, LR wins (although SVM is close) based on R-square.
Lowest
Highest
Lowest
LowestHighest
Lowest (tied
with SVM)
• Best Performing Algorithm

18. 181818
Regression Model: Accuracy (Cont’d)
Final Project
 While there is not much difference between the models, SVM provided
the highest lift of 2.329 in the top decile with overall average donation of
$16 and total dollar amount of $8,921 using transformed target variable.
• Best Performing Algorithm
Highest
Lift
Highest
Total
Amount
Average
Donation
Top
Decile

19. 191919
Regression Model: Accuracy (Cont’d)
• Regression Accuracy (Results on Validation Data)
Highest
Lift
Highest
Total
Amount
Average
Donation
Top
Decile
 Using the validation data of 96k responders and non-responders with the
target variable transformed, SVM provided the highest lift among other
models. The lift is 1.692 in the top decile with overall average donation of
$0.79 and total dollar amount of $12,330.
 This tells us that if we mailed to the top decile, we can expect $12,330.

20. 202020
Regression Model: Interpretation
• Best Predictors from SVM:
 Shown above is the output from KNIME from Linear Correlation to find
best predictors. The best predictors have the highest prediction values.
The output is sorted by prediction values. The top two variables given the
highest prediction values are LASTGIFT (transformed into log10) with
prediction probability of 0.971, and AVGGIFT with prediction probability
of 0.644.
 It makes sense that to be able to predict how much will be donated, it’s
important to consider first how much was the last donation, and how
much is the average donation so far for a particular donor.

21. 212121
Regression Model: Interpretation (Cont’d)
• Best Predictors from SVM Model:
 The scatter plot of one of the best
predictors from SVM, AVGGIFT, and the
target variable, TARGET_D is shown on
the left. It shows that there is a linear
relationship between AVGGIFT and
TARGET_D.
 It shows that as the average gift of a
donor increases, the donation amount
that he will give also increases.

22. 222222
Regression Model: Interpretation (Cont’d)
• Best Predictors from Linear Regression:
 The best predictors chosen by Linear
Regression based on significant P-values are
LASTGIFT, RFA_2F, F_RFA_2A, E_RFA_2A,
G_RFA_2A.
 LASTGIFT has a positive coefficient which
means it is positively related to the target
variable. The larger the last gift of a donor, the
larger the probability that he will give again. If
he gave a lot before, he is likely to give again.
 The RFA_2F is negatively related to the target
variable. This means that the more frequently
a donor gave before, the smaller his donation.
 F_RFA_2A, E_RFA_2A, G_RFA_2A are
positively related to the target variable. They
add 0.086, 052 and 0.13, respectively, to the
donation.
P-value

23. 232323
Regression Model: Interpretation (Cont’d)
• Best Predictors from Linear Regression (Cont’d):
 The scatter plot of one of the best predictors
from Linear Regression, LASTGIFT, and
the target variable, TARGET_D is shown on
the left. It shows that there is a linear
relationship between LASTGIFT and
TARGET_D.
 Not only that it confirms what we found
earlier that the larger the last gift of a
donor, the larger the probability that he will
give again, the scatter plot gives us
additional knowledge – it shows that as the
last gift of a donor increases, the donation
amount that he will give also increases.
 The scatter plot shows the appropriateness
of the linearity of the regression function.

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Regression Model: Conclusion
• The donor’s last gift amount and average gift amount are the most
influential variables to determine how much would a donor donate again.
• If the results of the SVM model are implemented in the next campaign to
the list of responders only from the previous campaign, and we know the
model gives a cumulative lift of 2.329 in the top decile with overall average
donation of $16, we can expect to get a total donation of $8,921. Without a
model, the total donation is $3,822.
• If the results of the SVM model are implemented in the next campaign to
all responders and non-responders from the previous campaign, and we
know the model gives a cumulative lift of 1.692 in the top decile with
overall average donation of $0.79, we can expect to get a total donation of
$12,330.

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Conclusion
• My recommendation is to use the Logistic model if all we want is to identify
a donor and a non-donor. If we are also interested in the amount of
donation, my recommendation is to use the SVM model, although the Linear
Regression model will work just as well too.
• The validation dataset will provide the best estimate of the money the best
model will generate as it contains people who did not donate which is
realistic. The validation data will give us an overall average donation of
$1.28 (from $12339/9847 people in 1st decile) per person. However, if we
just want to get the highest average donation, the test data will give us $36
(from $8921/244 people in 1st decile) per person.

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Appendix
• Independent variables (predictor variables) used in the
models