Determining Predictors of H-1B ApprovalThen we used Job Clusters and other attributes building...

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Determining Predictors of H-1B Approval Wenhao Yu Will Badart Luke Duane

Transcript of Determining Predictors of H-1B ApprovalThen we used Job Clusters and other attributes building...

Page 1: Determining Predictors of H-1B ApprovalThen we used Job Clusters and other attributes building Decision Tree model, Naive Bayes model and MLP model to classify the approval status.

Determining Predictors of H-1B Approval

Wenhao Yu Will Badart Luke Duane

Page 2: Determining Predictors of H-1B ApprovalThen we used Job Clusters and other attributes building Decision Tree model, Naive Bayes model and MLP model to classify the approval status.

IntroductionWhat is the H-1B Visa?○ A visa in the US under the INA

○ Given out to foreign workers at US company○ In 2017, 350,000 individuals applied and

200,000 were approved.

Problem○ How can we predict the approval status of a given H-1B visa

application?

○ What tangential analyses provide tangible business value for companies sponsoring H-1B visas?

Page 3: Determining Predictors of H-1B ApprovalThen we used Job Clusters and other attributes building Decision Tree model, Naive Bayes model and MLP model to classify the approval status.

Data Set

Description○ Count: 3,002,458 & Year: 2011-2016○ Attribute: Status (Y or N) / Employer name(Nominal) / Job title (Nominal) /

Full time(Y or N) / Wage(Numerical) / Year(Numerical) / Worksite(Numerical)

○ Salary : Mean 63,658 / Median 62,525 / standard deviation 12,500

The data we used is from Kaggle. It contains over 3 million records and tracks 10 different features per application. https://www.kaggle.com/asavla/h1-visa/data

Data Cleaning○ Delete incomplete data○ Move outlier (wage)

https://www.kaggle.com/asavla/h1-visa/data
Page 4: Determining Predictors of H-1B ApprovalThen we used Job Clusters and other attributes building Decision Tree model, Naive Bayes model and MLP model to classify the approval status.

The Road MapBefore Milestone :

1. Described and visualized the data2. Cleaned the data3. Implemented simple NB and DT model4. Evaluated the results

After Milestone:1. Figured out the model issues

(Overfitting / Zero Probability ...)1. Clustered the Job Title2. Tested varying parameters3. Re-implemented NB, DT and MLP model4. Evaluated the results

Page 5: Determining Predictors of H-1B ApprovalThen we used Job Clusters and other attributes building Decision Tree model, Naive Bayes model and MLP model to classify the approval status.

Clustering Job TitlesReason:1. 8,536 of different job titles, giving us no true information.2. Overfitting problems in Decision Tree model.Difficult:

1. All the job titles were in strings

Page 6: Determining Predictors of H-1B ApprovalThen we used Job Clusters and other attributes building Decision Tree model, Naive Bayes model and MLP model to classify the approval status.

Methodology --Job Titles to clusters

1. A simple list of string is invalid 2. Vectorize the Job Title3. Transform into sparse matrix of word counts 4. Structure is very high dimensional 5. Dimension reduction through SVD

Page 7: Determining Predictors of H-1B ApprovalThen we used Job Clusters and other attributes building Decision Tree model, Naive Bayes model and MLP model to classify the approval status.

Decision Tree Analysis1. Attribute: Job Cluster (Ordinal) / Full time(Y or N) / Wage(Numerical) /

Year(Numerical) / Worksite(Numerical)2. Label: Status (Yes/CERTIFIED or No/NOT CERTIFIED)3. Parameter: (1) Gini/ Entropy (2)Best/ Random (3) Depth=4

Best Feature: WageWorst Feature: Full Time Position

Page 8: Determining Predictors of H-1B ApprovalThen we used Job Clusters and other attributes building Decision Tree model, Naive Bayes model and MLP model to classify the approval status.

Decision Tree ModelGini & BestDepth : 4

Decision Tree ModelGini & Random

Depth : 4

Page 9: Determining Predictors of H-1B ApprovalThen we used Job Clusters and other attributes building Decision Tree model, Naive Bayes model and MLP model to classify the approval status.

Decision Tree ModelEntropy & Best

Depth : 4

Decision Tree ModelEntropy & Random

Depth : 4

Page 10: Determining Predictors of H-1B ApprovalThen we used Job Clusters and other attributes building Decision Tree model, Naive Bayes model and MLP model to classify the approval status.

Decision Tree EvaluationGini & Best Predicted

ApprovalPredicted Denied

True Approval 585178 43153

True Denied 66215 24896

Acc: 84.7% F1: 0.914 Pre: 0.898

Entropy & Best

Predicted Approval

Predicted Denied

True Approval

585789 43542

True Denied 66266 24845

Acc: 84.9% F1: 0.915 Pre: 0.898

Gini & Random

Predicted Approval

Predicted Denied

True Approval 585552 42779

True Denied 66255 24856

Acc: 84.8% F1: 0.915 Pre: 0.898

Entropy & Random

Predicted Approval

Predicted Denied

True Approval 585940 42391

True Denied 66555 24556

Acc: 84.7% F1: 0.914 Pre: 0.898

Best

Page 11: Determining Predictors of H-1B ApprovalThen we used Job Clusters and other attributes building Decision Tree model, Naive Bayes model and MLP model to classify the approval status.

Gaussian Naive Bayes

Evaluation Predicted Approval

Predicted Denied

True Approval 627,845 13

True Denied 91,134 450

Acc: 87.33% F1: 0.9333 Pre: 0.8732

1. Attribute: Job Cluster (Ordinal) / Full time(Y or N) / Wage(Numerical) / Year(Numerical) / Worksite(Numerical)

2. Label: Status (Y or N)3. Parameter: (1) Laplace Smoothing to avoid Zero Probability

Page 12: Determining Predictors of H-1B ApprovalThen we used Job Clusters and other attributes building Decision Tree model, Naive Bayes model and MLP model to classify the approval status.

MLP Classifier

1. Attribute: Job Cluster (Ordinal) / Full time(Y or N) / Wage(Numerical) /

Year(Numerical) / Worksite(Numerical)1. Label: Status (Y or N)2. Parameter:

(1) hidden layer size(2) activation (relu, identity, logistic, tanh) (3) solver (adam, lbfgs, sgm)

Page 13: Determining Predictors of H-1B ApprovalThen we used Job Clusters and other attributes building Decision Tree model, Naive Bayes model and MLP model to classify the approval status.

Model Compare

Model Time

DT 18.093s

NB 1.932s

MLP 46.78s

Page 14: Determining Predictors of H-1B ApprovalThen we used Job Clusters and other attributes building Decision Tree model, Naive Bayes model and MLP model to classify the approval status.

Conclusion

In this work, we proposed a series of process to cluster the Job Title, making it into an viable attribute.

Then we used Job Clusters and other attributes building Decision Tree model, Naive Bayes model and

MLP model to classify the approval status. The best model is Naive Bayes, which offers us a very high

accuracy (87%). Therefore, that will be very useful to justify the status of H-1B visa of applicants.

Page 15: Determining Predictors of H-1B ApprovalThen we used Job Clusters and other attributes building Decision Tree model, Naive Bayes model and MLP model to classify the approval status.

Concluding Insights: what could make this better?● More attributes

● Where are these applicants from?

● What’s their education level?● How much work experience do they have?

● Considerations of American Foreign Policy● How have laws changed?

● Should we give more/less weight to certain years that might be stricter/more lenient in acceptance? How would

that be quantified?


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