A mini-project to demonstrate how to use the "stacking" technique improving the model prediction.

1. What factors make a Young Adult author
more likely to be successful?
Yifan Wang

2. Understand what features contribute to the
success of young adult authors.
Model Explanation
What factors make a Young Adult author
more likely to be successful?
Research Question & Available Data
Explorative Data Analysis
Building Predictive Models
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Table of Content
Explore both categorical and numerical
variables
In this analysis, we will build multiple models
and stack them together to increase the overall
performance.

3. Key Findings
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• Young Adult Books with optimal length are more likely to be successful (between 290 and 485
pages);
• Book title with strong sentiments are more likely to be successful;
• Publisher does matter, certain publishers contribute to the success of the young adult book
authors.

4. Book Title Author Name Star Rating Number of reviews Length Publisher
Mistrust Margaret McHeyzer 4.5 64 333 Amazon
Girl in Pieces Kathleen Glasgow 4.5 139 418 Delacorte
Just Juliet Charlotte Reagan 4.5 369 224 Inkitt
Dork in Love ~ Tales of My Dorky Love Life: Teen
Romance
Ann Writes 4.5 9 122 Amazon
Warrior Cats: Battle (Warrior Cats (Werecat YA
Paranormal) Book 4)
Tiyana Angel 5 1 52 Guardian Angel Press
Out of Beat (Boys of Fallout Book 1) Cassandra Giovanni 4.5 11 231 Show not Tell Publishing
Research Question and Available Data
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With the Book Title, we can further extract
sentiment information to provide additional
features for this analysis.
With the Author Name, we can further extract
the first name and get gender information and
add to the features.
In this analysis, we will define success as a rating
of 4.5 or above and a reviewer count of 100 or
above.
What factors make a Young Adult author more
likely to be successful?
Anger Anticipation Disgust Fear Joy
0 0 1 1 0
0 0 0 0 0
0 0 0 0 0
0 1 0 1 1
1 0 0 1 0
0 0 0 0 0
Name Male % Female % Gender Year_Min Year_Max
Abigail 0.0019 0.9981 female 1920 2010
Addison 0.1359 0.8641 female 1920 2010
Alexandra 0.0039 0.9961 female 1920 2010
Amy 0.0026 0.9974 female 1920 2010
Amy 0.0026 0.9974 female 1920 2010
Ann 0.0029 0.9971 female 1920 2010

5. Explorative Data Analysis: Categorical Features
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Success by Book Title Sentiment Type Success by Sentiments

6. Explorative Data Analysis: Categorical Features
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Success by Publishers

7. 6
Explorative Data Analysis: Numerical Features
Success by Length Success by Star Rating Success by Number of Review

8. Building Predictive Models: Preprocess Data
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SMOTE: Synthetic Minority Over-Sampling TechniqueImbalanced Data
No Yes Total
73 27 100
73% 27%
No Yes Total
54 21 75
72% 28%
No Yes Total
42 42 84
50% 50%
All Available data Training Data Set Training Data Set (with SMOTE)

9. Accuracy Min. 1st Qu. Median Mean 3rd Qu. Max.
c5.0 0.6470588 0.7500000 0.7647059 0.7552288 0.7745098 0.8333333
gbm 0.5882353 0.7169118 0.7638889 0.7504085 0.8038194 0.8823529
treebag 0.6470588 0.6571691 0.7500000 0.7428105 0.8005515 0.8888889
rf 0.6470588 0.7169118 0.7712418 0.7559232 0.8038194 0.8235294
knn 0.5294118 0.6571691 0.7326389 0.7320670 0.8207721 0.8750000
SVMRadial 0.5882353 0.6305147 0.6875000 0.6841912 0.7181373 0.8125000
Building Predictive Models: Training Models
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Kappa Min. 1st Qu. Median Mean 3rd Qu. Max.
c5.0 0.31081081 0.5000000 0.5310122 0.5129304 0.5502283 0.6666667
gbm 0.17931034 0.4291958 0.5277778 0.5004806 0.6076389 0.7671233
treebag 0.30136986 0.3268581 0.5000000 0.4895214 0.6006944 0.7777778
rf 0.30136986 0.4353448 0.5416667 0.5122408 0.6076389 0.6433566
knn 0.04225352 0.3125000 0.4652778 0.4621085 0.6424569 0.7500000
SVMRadial 0.17931034 0.2604167 0.3750000 0.3715983 0.4407159 0.6250000
Correlation c5.0 gbm treebag rf knn SVMRadial
c5.0 1.0000000 0.5614717 0.6228355 0.62473600 0.38103898 0.1938362
gbm 0.5614717 1.0000000 0.2465510 0.32106821 0.81746483 0.6061850
treebag 0.6228355 0.2465510 1.0000000 0.38551636 0.37732381 0.5382060
rf 0.6247360 0.3210682 0.3855164 1.0000000 -0.06350643 -0.1650719
knn 0.3810390 0.8174648 0.3773238 -0.06350643 1.0000000 0.8849458
SVMRadial 0.1938362 0.6061850 0.5382060 -0.16507190 0.88494583 1.0000000
Sub-Models Results Sub-Models Correlation

10. 9
Reference
Prediction No Yes
No 15 2
Yes 4 4
Reference
Prediction No Yes
No 0.60 0.08
Yes 0.16 0.16
Factor Probability
1 No 0.042
2 No 0.096
3 No 0.290
4 Yes 0.642
5 Yes 0.516
6 No 0.154
7 No 0.084
8 No 0.030
9 No 0.128
10 No 0.274
Building Predictive Models: Stacking Sub-Models
Accuracy 0.76
95% CI (0.5487, 0.9064)
Kappa 0.4094
Sensitivity 0.6667
Specificity 0.7895
Pos Pred. Value 0.5000
Neg Pred. Value 0.8824
Prevalence 0.2400
Detection Rate 0.1600
Detection Prevalence 0.3200
Balanced Accuracy 0.7281
Final Model Stacked with Random Forest Sub-Model (Random Forest)
Accuracy 0.56
95% CI (0.3493, 0.756)
Kappa 0.1379
Sensitivity 0.6667
Specificity 0.5263
Pos Pred. Value 0.3077
Neg Pred. Value 0.8333
Prevalence 0.2400
Detection Rate 0.1600
Detection Prevalence 0.5200
Balanced Accuracy 0.5965
Prediction Accuracy & Statistics
Confusion Matrix ROC Curve (AUC: 0.7368421)
Accuracy & Statistics
ROC Curve (AUC: 0.7105263)

11. Model Explanation
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Feature Importance Heatmap (all 25 cases in test data set)

12. Model Explanation
Feature Importance Visualization (First 6 cases from test data set)
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