Loan Default Prediction with Machine Learning
•
9 likes•6,931 views
See webinar recording of this presentation at: https://resource.alibabacloud.com/webinar/detail.htm?webinarId=50 This webinar is designed to help users understand the end-to-end data science processes of using a propensity model on Alibaba Cloud’s Machine Learning Platform for AI; from defining the business problem, exploratory data analysis, data processing, model training to testing and deployment. You get an end-to-end case study (including a live demo) on how to use Alibaba Cloud products to predict the propensity of loan defaults. Learn more about Machine Learning Platform for AI: https://www.alibabacloud.com/product/machine-learning
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Similar to Loan Default Prediction with Machine Learning
Similar to Loan Default Prediction with Machine Learning (20)
More from Alibaba Cloud
More from Alibaba Cloud (20)
Recently uploaded
Recently uploaded (20)
Loan Default Prediction with Machine Learning
- 1. Predicting Propensity to Default using PAI Pradeep Menon, Director of Big Data and AI Solutions, Alibaba Cloud @rpradeepmenon pradeep.menon@alibaba-inc.com
- 2. Overview 01 02 Quick introduction to MaxCompute and PAI End-End Data Science: Predict propensity to default
- 3. MaxCompute Large Scale Data Processing Key Features Peta-byte level scaling Multiple computational models (graphs, sql, MR) Safe and reliable Rich developer ecosystem (python, java, R) Cost-effective PaaS offering$
- 4. Machine Learning PAI 100+ data science components Sophisticated Machine Learning Algorithms Drag & Drop interface + SDK Support for deep learning frameworks (TensorFlow, Caffe, MXNet)^ GPU^ + CPU Support Machine Learning at Scale Key Features ^ Coming Soon
- 5. PAI Algorithm Catalog Time Series ARIMA Auto ARIMA Regression Gradient Boosted Regression Linear Regression PS-SMART Regression PS-Linear Regression Network Analytics Tree Depth K-Core Single Source Shortest Path Page Rank Label Propagation Clustering Modularity Point Clustering Coefficient Edge Clustering Coefficient Multiclass Classification PS-SMART Regression K-NN Logistic Regression Random Forest Naive Bayes Binary Classification Gradient Boosted Decision Tree PS-SMART Binary Classification Linear SVM Logistic Regression Model Evaluation Binary Classification Evaluation Regression Model Evaluation Clustering Evaluation Confusion Matrix Multi Class Evaluation Clustering K-Means Clustering Recommendation Collaborative Filtering Text Analytics Split Word String Similarity ngram Count Text Summarization Keyword Extraction Sentence Splitting Semantic Vector Doc2Vec CRF Article Similarity Word2Vec TF-IDF PLDA SVD
- 6. Predict who will default based on input parameters Business Problem
- 7. Define Business Problem Map to Machine Learning Problem Data Preparation Exploratory Data Analysis Modeling Evaluation • Clearly defined business problem • Set success criteria • Define clear data science objectives • Understand data points and constraints • Formulate data analytics strategy • Perform required transformation • Experiment with multiple models • Choose the most optimal model • Create a feedback loop • Break business problems to data science problems • Identify Machine Learning Problem categories • Perform statistical and visual analysis • Discover and handle outliers/errors • Shortlist predictive modelling techniques 80% of work 20% of work Data Science Process
- 9. Data Laon Status Fully Paid Charged off i.e. Default Categorical Loan Status Annual Income Credit Score Years in Current Job Home Ownership Purpose Laon Amount Term Unique Loan Id Customer Id Loan Data Tax Liens Bankruptcies Binary Numeric Credit Score Loan Amount Term Years in Current Job Home Ownership Annual Income Years of Credit History Months Since Last Delinquent Number of Open Accounts Number of Credit Problems Current Credit Balance Maximum Open Credit Purpose Monthly Debt
- 10. Machine Learning Tasks Machine Learning Unsupervised Has Target Specific Purpose No Target Exploratory Types Clustering Link Prediction Data Reduction Categorical Target Will Churn or Not? Numeric Target Continuous Variable E.g. Predicting car price for next month Often Binary Creating Unknown Segments Recommendation Engines Dimensionality Reduction Supervised Types Classification Regression 1 2
- 11. Get Data SQL Script Transformation Fill Missing Values Normalize Data Exploratory Data Analysis Exploration and Data Pre-processing
- 12. Normalization What 1 Re-scales Numeric Values Brings Them to Same Scale Eliminates Skew Improves Model Performance Buffers from Unseen Variability Alleviates Outlier Impacts 2 FrequentTypes 3 Z-score Min-max Why? Mean Standard Deviation Transform to a Range -1..1 0..1 Normalisation
- 13. Data Training Set Testing Set Derive Model Test Model Estimate Accuracy/ Reduce Error Refine Model Unseen Data Split Data
- 14. FPR TPR 1 1 0 AUC > 0.99 ”May be overfitting” AUC = 0.9 ”A better model” AUC >= 0.7 ”A good model” AUC > 0.5 ”Better use a coin” Target AUC: 0.85 Binary Classification Evaluation
- 15. Time Series ARIMA Auto ARIMA Regression Gradient Boosted Regression Linear Regression PS-SMART Regression PS-Linear Regression Network Analytics Tree Depth K-Core Single Source Shortest Path Page Rank Label Propagation Clustering Modularity Point Clustering Coefficient Edge Clustering Coefficient Multiclass Classification PS-SMART Regression K-NN Logistic Regression Random Forest Naive Bayes Binary Classification Gradient Boosted Decision Tree PS-SMART Binary Classification Linear SVM Logistic Regression Model Evaluation Binary Classification Evaluation Regression Model Evaluation Clustering Evaluation Confusion Matrix Multi Class Evaluation Clustering K-Means Clustering Recommendation Collaborative Filtering Text Analytics Split Word String Similarity ngram Count Text Summarization Keyword Extraction Sentence Splitting Semantic Vector Doc2Vec CRF Article Similarity Word2Vec TF-IDF PLDA SVD Lets Choose 2 Algorithms LOGISTIC REGRESSION RANDOM FOREST
- 16. End to End Experiment Flow
- 17. AUC = 0.6456 AUC = 0.8151 Target AUC: 0.80 LOGISTIC REGRESSION RANDOM FOREST Choose the Best Model
- 18. Deployment Model Creation Schedule the model in Data Works Choose Best Model Create Deployment Experiment Write Predictions to MaxCompute Deploy the model to DataWorks for periodic execution Model Deployment and Schedule
- 19. PAI enables Machine Learning at scale Easy to use platform for AI Enables rapid deployment of model for faster insight Top 3 Takeaways