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Please enable JavaScript in your browser settings or update your browser.Challenge 3 | Moving on to Tasks
Data Preprocessing
course content

Зміст курсу

Data Preprocessing

Data Preprocessing

1. Brief Introduction
Data TypesData Processing MethodsDataset: Test and TrainingDeleting an "Extra" DataChanging the Data Type
2. Processing Quantitative Data
Data ScalingData Scaling vs Data NormalizationRemoving OutliersRemoving Missing ValuesData Augmentation: Synthetic Data
3. Processing Categorical Data
Methods for Encoding the Categorical DataOne-Hot EncodingOrdinal EncodingLabel Encoding of the Target VariableChallenge
4. Time Series Data Processing
Data Type ConversionData CleaningStationarityDenoisingTrain/Test Split & Cross ValidationChallenge
5. Feature Engineering
Technique IdeaRealizationFeature Extraction from TextFeature Extraction from ImagesFeature Extraction from Time SeriesChallenge
6. Moving on to Tasks
Challenge 1Challenge 2Challenge 3

Challenge 3

Завдання

The last task we have prepared for you is the implementation of feature engineering. You will be working with the 'sales_data.csv' dataset, and your task will be to create new variables and process categorical and numeric data.

  1. Use feature engineering to create new columns such as year, month, and day of the week Date
  2. Encode the 'Region' and 'Product; categorical columns with the ohe-hot encoding method
  3. For numeric data ('Sales'), you will need to scale the data

Завдання

The last task we have prepared for you is the implementation of feature engineering. You will be working with the 'sales_data.csv' dataset, and your task will be to create new variables and process categorical and numeric data.

  1. Use feature engineering to create new columns such as year, month, and day of the week Date
  2. Encode the 'Region' and 'Product; categorical columns with the ohe-hot encoding method
  3. For numeric data ('Sales'), you will need to scale the data

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Секція 6. Розділ 3
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Challenge 3

Завдання

The last task we have prepared for you is the implementation of feature engineering. You will be working with the 'sales_data.csv' dataset, and your task will be to create new variables and process categorical and numeric data.

  1. Use feature engineering to create new columns such as year, month, and day of the week Date
  2. Encode the 'Region' and 'Product; categorical columns with the ohe-hot encoding method
  3. For numeric data ('Sales'), you will need to scale the data

Завдання

The last task we have prepared for you is the implementation of feature engineering. You will be working with the 'sales_data.csv' dataset, and your task will be to create new variables and process categorical and numeric data.

  1. Use feature engineering to create new columns such as year, month, and day of the week Date
  2. Encode the 'Region' and 'Product; categorical columns with the ohe-hot encoding method
  3. For numeric data ('Sales'), you will need to scale the data

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Секція 6. Розділ 3
toggle bottom row

Challenge 3

Завдання

The last task we have prepared for you is the implementation of feature engineering. You will be working with the 'sales_data.csv' dataset, and your task will be to create new variables and process categorical and numeric data.

  1. Use feature engineering to create new columns such as year, month, and day of the week Date
  2. Encode the 'Region' and 'Product; categorical columns with the ohe-hot encoding method
  3. For numeric data ('Sales'), you will need to scale the data

Завдання

The last task we have prepared for you is the implementation of feature engineering. You will be working with the 'sales_data.csv' dataset, and your task will be to create new variables and process categorical and numeric data.

  1. Use feature engineering to create new columns such as year, month, and day of the week Date
  2. Encode the 'Region' and 'Product; categorical columns with the ohe-hot encoding method
  3. For numeric data ('Sales'), you will need to scale the data

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Завдання

The last task we have prepared for you is the implementation of feature engineering. You will be working with the 'sales_data.csv' dataset, and your task will be to create new variables and process categorical and numeric data.

  1. Use feature engineering to create new columns such as year, month, and day of the week Date
  2. Encode the 'Region' and 'Product; categorical columns with the ohe-hot encoding method
  3. For numeric data ('Sales'), you will need to scale the data

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Секція 6. Розділ 3
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
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