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US-census_population_segmentation_PCA_Kmeans
breast_cancer_prediction
ensemble_modeling
fair_linear_learner
gluon_recommender_system
linear_time_series_forecast
ntm_20newsgroups_topic_modeling
video_game_sales
xgboost_customer_churn
xgboost_direct_marketing
README.md

README.md

Amazon SageMaker Examples

Introduction to Applying Machine Learning

These examples provide a gentle introduction to machine learning concepts as they are applied in practical use cases across a variety of sectors.

  • Targeted Direct Marketing predicts potential customers that are most likely to convert based on customer and aggregate level metrics, using Amazon SageMaker's implementation of XGBoost.
  • 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.
  • Time-series Forecasting generates a forecast for topline product demand using Amazon SageMaker's Linear Learner algorithm.
  • 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.