These examples provide a gentle introduction to machine learning concepts as they are applied in practical use cases across a variety of sectors.
- Predicting Customer Churn uses customer interaction and service usage data to find those most likely to churn, and then walks through the cost/benefit trade-offs of providing retention incentives. This uses Amazon SageMaker's implementation of XGBoost to create a highly predictive model.
- Cancer Prediction predicts Breast Cancer based on features derived from images, using SageMaker's Linear Learner.
- Ensembling predicts income using two Amazon SageMaker models to show the advantages in ensembling.
- Video Game Sales develops a binary prediction model for the success of video games based on review scores.
- MXNet Gluon Recommender System uses neural network embeddings for non-linear matrix factorization to predict user movie ratings on Amazon digital reviews.
- Fair Linear Learner is an example of an effective way to create fair linear models with respect to sensitive features.
- Population Segmentation of US Census Data using PCA and Kmeans analyzes US census data and reduces dimensionality using PCA then clusters US counties using KMeans to identify segments of similar counties.
- Document Embedding using Object2Vec is an example to embed a large collection of documents in a common low-dimensional space, so that the semantic distances between these documents are preserved.
- Traffic violations forecasting using DeepAR is an example to use daily traffic violation data to predict pattern and seasonality to use Amazon DeepAR alogorithm.