├── .DS_Store ├── 10_imbalanced_data ├── aug_test.csv ├── aug_train.csv ├── dealing-with-imbalanced-data.ipynb └── sample_submission.csv ├── 11_modeling ├── healthcare-dataset-stroke-data.csv └── modeling-process-basics.ipynb ├── 12_model_evaluation ├── .ipynb_checkpoints │ ├── classification_metrics-checkpoint.ipynb │ └── regression_metrics-checkpoint.ipynb ├── classification_metrics.ipynb ├── housing_data.csv ├── ifood_df.csv └── regression_metrics.ipynb ├── 365datascience_ml_process_flashcards.apkg ├── 5_data_preprocessing ├── .DS_Store ├── 5_1_missing_values │ ├── .DS_Store │ ├── .ipynb_checkpoints │ │ ├── Cross Validation-checkpoint.ipynb │ │ └── section_5_missing_values-checkpoint.ipynb │ ├── clv_data.csv │ ├── section_5_missing_values.ipynb │ └── utils.py └── 5_2_outliers │ ├── .DS_Store │ ├── .ipynb_checkpoints │ ├── Outliers-checkpoint.ipynb │ └── section_5_outliers-checkpoint.ipynb │ ├── clv_data.csv │ └── section_5_outliers.ipynb ├── 6_exploratory_data_analysis ├── .DS_Store ├── Aggregated_Metrics_By_Country_And_Subscriber_Status.csv ├── Aggregated_Metrics_By_Video.csv ├── All_Comments_Final.csv ├── Video_Performance_Over_Time.csv └── basic-eda-example.ipynb ├── 7_feature_engineering ├── .DS_Store ├── 7_1_categorical_features │ ├── .DS_Store │ ├── .ipynb_checkpoints │ │ ├── Section 7.1 Categorical Features-checkpoint.ipynb │ │ └── section_7_1_categorical_features-checkpoint.ipynb │ ├── AB_NYC_2019.csv │ ├── airbnb_dataset_ml_process.csv │ └── section_7_1_categorical_features.ipynb └── 7_2_scaling │ └── feature-scaling.ipynb ├── 8_cross_validation ├── .DS_Store ├── .ipynb_checkpoints │ ├── Cross Validation-checkpoint.ipynb │ └── section_8_cross_validation-checkpoint.ipynb ├── fraud_data.csv └── section_8_cross_validation.ipynb ├── 9_feature_selection ├── .DS_Store ├── .ipynb_checkpoints │ └── section_9_feature_selection-checkpoint.ipynb ├── BankChurners.csv └── section_9_feature_selection.ipynb ├── README.md ├── dataset_simulation.py ├── 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