├── .github └── workflows │ └── latest_dependency_checker.yml ├── .gitignore ├── LICENSE ├── Makefile ├── README.md ├── archived-notebooks ├── README.md ├── predict-appointment-noshow │ ├── README.md │ ├── Tutorial.ipynb │ ├── evalml_horizontal.png │ └── utils.py ├── predict-correct-answer │ ├── Demo - DataShop.ipynb │ ├── README.md │ ├── entityset_function.ipynb │ ├── evalml_horizontal.png │ └── utils.py ├── predict-credit-churn │ ├── CreditChurn.ipynb │ └── README.md ├── predict-customer-churn │ ├── README.md │ ├── churn │ │ ├── 1. Partitioning Data.ipynb │ │ ├── 2. Prediction Engineering.ipynb │ │ ├── 3. Feature Engineering.ipynb │ │ ├── 4. Feature Engineering on Spark.ipynb │ │ ├── 5. Modeling.ipynb │ │ ├── best_pipeline.py │ │ └── best_pipeline_new.py │ └── images │ │ ├── AutomatedProcess.png │ │ ├── Churn Defined.png │ │ ├── Churn Parameters.png │ │ ├── FeatureLabs.png │ │ ├── Framework - Feature Engineering.png │ │ ├── Framework - Modeling.png │ │ ├── Framework-PredictionEngineering.png │ │ ├── Framework.png │ │ ├── ManualProcess.png │ │ ├── ProcessesDiagram.png │ │ ├── aws_status.png │ │ ├── churn_definition.png │ │ ├── churn_definition_max.png │ │ ├── completed_labeling.PNG │ │ ├── confusion_matrix_rf.png │ │ ├── cover_modeling.png │ │ ├── cover_overview.png │ │ ├── cover_prediction.png │ │ ├── cutoff_times_single.png │ │ ├── data_schema.png │ │ ├── distributed_architecture.png │ │ ├── example_feature_dist.png │ │ ├── example_features.png │ │ ├── example_predictions.PNG │ │ ├── executors.png │ │ ├── feature_engineering_cover.png │ │ ├── feature_engineering_process.png │ │ ├── featuretools-logo.png │ │ ├── final_metrics.png │ │ ├── label_times_example.png │ │ ├── label_times_table.png │ │ ├── labeling_application.PNG │ │ ├── labeling_event_timeline.PNG │ │ ├── labeling_task_info.PNG │ │ ├── lead_time_performance.png │ │ ├── logs_df.PNG │ │ ├── max-primitive.png │ │ ├── max-primitive.svg │ │ ├── members_df.PNG │ │ ├── metrics_three_models.png │ │ ├── modeling_process.png │ │ ├── most_important_rf.png │ │ ├── one_feature_tree.png │ │ ├── overall_framework.png │ │ ├── pandas-logo.png │ │ ├── placeholder_cover.jpeg │ │ ├── pr_curve_tuned.png │ │ ├── precision_recall_rf.png │ │ ├── prediction_engineering_process.png │ │ ├── prediction_table.png │ │ ├── predictions_ecdf.png │ │ ├── spark-logo-trademark.png │ │ ├── spark_cluster.png │ │ ├── spark_cluster2.png │ │ ├── spark_cluster_3_workers.png │ │ ├── spark_cluster_main.png │ │ ├── spark_job.png │ │ ├── spark_single1.png │ │ ├── spark_single_machine.PNG │ │ ├── stages.png │ │ ├── time_since_last.png │ │ ├── time_spent_last_month.png │ │ ├── total_previous_month.png │ │ └── transactions_df.PNG ├── predict-household-poverty │ ├── README.md │ └── Tutorial.ipynb ├── predict-housing-prices │ ├── HousingPrices-Demo.ipynb │ └── README.md ├── predict-loan-repayment │ ├── Automated Loan Repayment.ipynb │ └── README.md ├── predict-malicious-cyber-connections │ ├── Create Feature Matrices from LL Cyber Data.ipynb │ ├── Predicting Malicious Cyber Connections with Featuretools.ipynb │ ├── README.md │ └── utils.py ├── predict-next-purchase │ ├── README.md │ ├── Tutorial.ipynb │ ├── process_data.py │ └── utils.py ├── predict-olympic-medals │ ├── BaselineSolution.ipynb │ ├── PredictOlympicMedals.ipynb │ ├── README.md │ ├── images │ │ ├── Olympic-logo.png │ │ ├── cm_1984.png │ │ └── cm_2004.png │ └── utils.py ├── predict-remaining-useful-life │ ├── Advanced Featuretools RUL.ipynb │ ├── README.md │ ├── Simple Featuretools RUL Demo.ipynb │ └── utils.py └── predict-taxi-trip-duration │ ├── NYC Taxi 1 - Mean Duration.ipynb │ ├── NYC Taxi 2 - Baseline Solution.ipynb │ ├── NYC Taxi 3 - Simple Featuretools.ipynb │ ├── NYC Taxi 4 - Custom Featuretools Primitives.ipynb │ ├── README.md │ ├── img │ ├── bar_graph_scores.png │ ├── dataset_sample.png │ ├── featuretools-logo.png │ ├── pipeline.png │ └── rank.png │ └── taxi_utils.py ├── predict-appointment-noshow ├── README.md ├── Tutorial.ipynb ├── data │ └── KaggleV2-May-2016.csv ├── evalml_horizontal.png └── utils.py ├── predict-correct-answer ├── Demo - DataShop.ipynb ├── README.md ├── data │ ├── ds76_tx_All_Data_74_2018_0912_070949.txt │ └── images │ │ ├── difficulty_vs_time.html │ │ └── exampleimage.png ├── entityset_function.ipynb ├── evalml_horizontal.png └── utils.py ├── predict-credit-churn ├── CreditChurn.ipynb ├── README.md └── data │ └── BankChurners.csv ├── predict-customer-churn ├── README.md ├── churn │ ├── 1. Partitioning Data.ipynb │ ├── 2. Prediction Engineering.ipynb │ ├── 3. Feature Engineering.ipynb │ ├── 4. Feature Engineering on Spark.ipynb │ ├── 5. Modeling.ipynb │ ├── best_pipeline.py │ └── best_pipeline_new.py └── images │ ├── AutomatedProcess.png │ ├── Churn Defined.png │ ├── Churn Parameters.png │ ├── FeatureLabs.png │ ├── Framework - Feature Engineering.png │ ├── Framework - Modeling.png │ ├── Framework-PredictionEngineering.png │ ├── Framework.png │ ├── ManualProcess.png │ ├── ProcessesDiagram.png │ ├── aws_status.png │ ├── churn_definition.png │ ├── churn_definition_max.png │ ├── completed_labeling.PNG │ ├── confusion_matrix_rf.png │ ├── cover_modeling.png │ ├── cover_overview.png │ ├── cover_prediction.png │ ├── cutoff_times_single.png │ ├── data_schema.png │ ├── distributed_architecture.png │ ├── example_feature_dist.png │ ├── example_features.png │ ├── example_predictions.PNG │ ├── executors.png │ ├── feature_engineering_cover.png │ ├── feature_engineering_process.png │ ├── featuretools-logo.png │ ├── final_metrics.png │ ├── label_times_example.png │ ├── label_times_table.png │ ├── labeling_application.PNG │ ├── labeling_event_timeline.PNG │ ├── labeling_task_info.PNG │ ├── lead_time_performance.png │ ├── logs_df.PNG │ ├── max-primitive.png │ ├── max-primitive.svg │ ├── members_df.PNG │ ├── metrics_three_models.png │ ├── modeling_process.png │ ├── most_important_rf.png │ ├── one_feature_tree.png │ ├── overall_framework.png │ ├── pandas-logo.png │ ├── placeholder_cover.jpeg │ ├── pr_curve_tuned.png │ ├── precision_recall_rf.png │ ├── prediction_engineering_process.png │ ├── prediction_table.png │ ├── predictions_ecdf.png │ ├── spark-logo-trademark.png │ ├── spark_cluster.png │ ├── spark_cluster2.png │ ├── spark_cluster_3_workers.png │ ├── spark_cluster_main.png │ ├── spark_job.png │ ├── spark_single1.png │ ├── spark_single_machine.PNG │ ├── stages.png │ ├── time_since_last.png │ ├── time_spent_last_month.png │ ├── total_previous_month.png │ └── transactions_df.PNG ├── predict-daily-temperature ├── Daily Temperature 1 - Baseline Solution.ipynb ├── Daily Temperature 2 - Featuretools Solution.ipynb ├── Daily Temperature 3 - EvalML Solution.ipynb ├── README.md ├── dataset │ └── DailyDelhiClimate.csv ├── figures │ ├── baseline.jpeg │ ├── compare_models.png │ ├── data_plot.png │ ├── random_forest.jpeg │ └── ts_parameter_viz.png └── utils.py ├── predict-household-poverty ├── README.md └── Tutorial.ipynb ├── predict-housing-prices ├── HousingPrices-Demo.ipynb └── README.md ├── predict-loan-repayment ├── Automated Loan Repayment.ipynb └── README.md ├── predict-malicious-cyber-connections ├── Create Feature Matrices from LL Cyber Data.ipynb ├── Predicting Malicious Cyber Connections with Featuretools.ipynb ├── README.md └── utils.py ├── predict-next-purchase ├── README.md ├── Tutorial.ipynb ├── process_data.py └── utils.py ├── predict-olympic-medals ├── BaselineSolution.ipynb ├── PredictOlympicMedals.ipynb ├── README.md ├── data │ └── olympic_games_data │ │ ├── dictionary.csv │ │ ├── num_medals_by_country_labels.csv │ │ ├── summer.csv │ │ └── winter.csv ├── images │ ├── Olympic-logo.png │ ├── cm_1984.png │ └── cm_2004.png └── utils.py ├── predict-remaining-useful-life ├── Advanced Featuretools RUL.ipynb ├── README.md ├── Simple Featuretools RUL Demo.ipynb └── utils.py ├── predict-taxi-trip-duration ├── NYC Taxi 1 - 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