Course Content
Data Preprocessing
Data Preprocessing
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.
- Use feature engineering to create new columns such as year, month, and day of the week
Date
- Encode the
'Region'
and'Product;
categorical columns with the ohe-hot encoding method - 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.
- Use feature engineering to create new columns such as year, month, and day of the week
Date
- Encode the
'Region'
and'Product;
categorical columns with the ohe-hot encoding method - For numeric data (
'Sales'
), you will need to scale the data
Everything was clear?
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.
- Use feature engineering to create new columns such as year, month, and day of the week
Date
- Encode the
'Region'
and'Product;
categorical columns with the ohe-hot encoding method - 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.
- Use feature engineering to create new columns such as year, month, and day of the week
Date
- Encode the
'Region'
and'Product;
categorical columns with the ohe-hot encoding method - For numeric data (
'Sales'
), you will need to scale the data
Everything was clear?
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.
- Use feature engineering to create new columns such as year, month, and day of the week
Date
- Encode the
'Region'
and'Product;
categorical columns with the ohe-hot encoding method - 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.
- Use feature engineering to create new columns such as year, month, and day of the week
Date
- Encode the
'Region'
and'Product;
categorical columns with the ohe-hot encoding method - For numeric data (
'Sales'
), you will need to scale the data
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.
- Use feature engineering to create new columns such as year, month, and day of the week
Date
- Encode the
'Region'
and'Product;
categorical columns with the ohe-hot encoding method - For numeric data (
'Sales'
), you will need to scale the data