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Animal Shelter Adoption

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Presentation on theme: "Animal Shelter Adoption"— Presentation transcript:

1 Animal Shelter Adoption
Best attribute for predicting outcome? Ryan Taber Hunter O’Rourke

2 The Data Size of Data: 26730 Animals with 10 attributes
Type of Data: Categorical: AnimalID Name DateTime OutcomeType OutcomeSubtype AnimalType SexuponOutcome AgeuponOutcome Breed Color A671945 Hambone :22:00 Return_to_owner Foster Dog Neutered Male 1 year Shetland Sheepdog Mix Brown / White A656520 Emily :44:00 Euthanasia Suffering Cat Spayed Female Domestic Shorthair Mix Cream Tabby

3 The Data (cont) Missing values: Data used: Names: Sometimes
OutcomeSubtype: Sometimes SexuponOutcome: value = Unknown Data used: SVM: Looking primarily at AgeuponOutcome, Breed, and Color Bayes: Ignored AnimalID, Name, DateTime, and OutcomeSubtype

4 Data Mining Tasks (Bayes)
Which attributes give the highest accuracy in predicting outcome? Naiive Bayes AnimalType SexuponOutcome AgeuponOutcome Breed Color 39.5% 40.1% 52.3% 31.2%

5 Data Mining Tasks (Bayes)
Reasons why it didn’t work well Unique breed 1200+ Unique colors 350+ Data needs significant preprocessing before it becomes useful

6 Data Mining Tasks (SVM)
Followed template of homework 4.1 Using svm.SVC Linear, Poly, RBF and Sigmoid kernels ovo, ovr Main questions: Age,Breed,Color combination on outcome Most impactful attribute on outcome Ideal feature adoption feature vector

7 Data Mining Tasks (SVM) - Process
Convert categorical variables to numerical Ran on all AnimalTypes and Type-specific Initial results for Age/Breed & Breed/Color Only for training data set Minimize Breed and Color sets Breeds --> ~1200 to ~350 Colors --> TBD

8 Data Mining Tasks (SVM)

9 Data Mining Tasks (SVM)

10 Data Mining Tasks (SVM)
Moving to Dog Exclusively (because they are better) Train: ~16000 Test: ~6650 Currently starting to ‘predict’ test data outcomes Very limited success so far Having trouble with prediction mainly

11 SVM vs Bayes Ultimately, final Kaggle test data classification is the best evaluation tool --> uses multi-class log loss grading function Could run k-folds cross validation on the training data

12 Other’s Findings In order variable importance using Random Forest
AgeinDays Intact Hour Color Month Weekday HasName

13 Future Work Significantly more preprocessing
Dates -> season, weekday/end, times, etc. Name -> has name, male/female/uni, etc.. Having Name + Age threshold more likely to be returned to Owner? Breeds -> reduce to dog types, short/long hair, etc.. Color -> simpler colors ex. light, dark, ginger

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