# 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.

- [LightGBM_Distributed_Training_Dask](sagemaker_lightgbm_distributed_training_dask) demonstrates the distributed training of Amazon SageMaker's implementation of [LightGBM](https://lightgbm.readthedocs.io/en/latest/) using [Dask](https://www.dask.org/).
- [Predicting Customer Churn](xgboost_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](https://github.com/dmlc/xgboost) to create a highly predictive model.
- [Predicting Customer Churn](lightgbm_catboost_tabtransformer_autogluon_churn) uses Amazon SageMaker's implementation of [LightGBM](https://lightgbm.readthedocs.io/en/latest/), [CatBoost](https://catboost.ai/), [TabTransformer](https://arxiv.org/abs/2012.06678), and [AutoGluon-Tabular](https://auto.gluon.ai/stable/index.html) with [SageMaker Automatic Model Tuning](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning.html) to create four predictive models on customer churn dataset, and evaluate their performance on the same test data.
- [Cancer Prediction](breast_cancer_prediction) predicts Breast Cancer based on features derived from images, using SageMaker's Linear Learner.
- [Ensembling](ensemble_modeling) predicts income using two Amazon SageMaker models to show the advantages in ensembling.
- [Video Game Sales](video_game_sales) develops a binary prediction model for the success of video games based on review scores.
- [MXNet Gluon Recommender System](gluon_recommender_system) uses neural network embeddings for non-linear matrix factorization to predict user movie ratings on Amazon digital reviews.
- [Fair Linear Learner](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](US-census_population_segmentation_PCA_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](object2vec_document_embedding) 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](deepar_chicago_traffic_violations) is an example to use daily traffic violation data to predict pattern and seasonality to use Amazon DeepAR alogorithm.
- [Visual Inspection Automation with Pre-trained Amazon SageMaker Models](visual_object_detection) is an example for fine-tuning pre-trained Amazon Sagemaker models on a target dataset.
- [Create SageMaker Models Using the PyTorch Model Zoo](sagemaker_pytorch_model_zoo) contains an example notebook to create a SageMaker model leveraging the PyTorch Model Zoo and visualize the results.
- [Deep Demand Forecasting](deep_demand_forecasting) provides an end-to-end solution for Demand Forecasting task using three state-of-the-art time series algorithms LSTNet, Prophet, and SageMaker DeepAR, which are available in GluonTS and Amazon SageMaker.
- [Credit Card Fraud Detector](credit_card_fraud_detector) is an example of the core of a credit card fraud detection system using SageMaker with Random Cut Forest and XGBoost.
- [Fraud Detection Using Graph Neural Networks](fraud_detection_using_graph_neural_networks) is an example to identify fraudulent transactions from transaction and user identity datasets.
- [Identify key insights from textual document](identify_key_insights_from_textual_document) contains comphrensive notebooks for five natural language processing tasks Document Summarization, Text Classification, Question and Answering, Name Entity Recognition, and Semantic Relation Extracion, and zero-shot prompt engineering to solve various NLP tasks using the state-of-the-art Flan T5 XL model.
- [Churn Prediction Multimodality of Text and Tabular](churn_prediction_multimodality_of_text_and_tabular) is an example notebook to train and deploy a churn prediction model that uses state-of-the-art natural language processing model to find useful signals in text. In addition to textual inputs, this model uses traditional structured data inputs such as numerical and categorical fields.