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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

Course Content

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

Task

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

Task

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

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 6. Chapter 3
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Challenge 3

Task

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

Task

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

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 6. Chapter 3
toggle bottom row

Challenge 3

Task

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

Task

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

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Task

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

Switch to desktop for real-world practiceContinue from where you are using one of the options below
Section 6. Chapter 3
Switch to desktop for real-world practiceContinue from where you are using one of the options below
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