Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Challenge 1 | 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 1

Task

In this challenge, you will need to work with the 'adult-census.csv' dataset. It contains both categorical and numerical data. Your task will be to prepare the data for processing.

  1. Read the dataset 'adult-census.csv'
  2. Explore the dataset. Carefully check which character indicates the missed data in the dataset and replace it with the np.nan object
  3. Remove rows with missing values
  4. Let's start with processing categorical data - columns 'workclass', 'sex' Use a one-hot encoding method to encode them
  5. For numeric data ('age', 'hours-per-week'), you will need to scale the data
  6. Print processed data

Task

In this challenge, you will need to work with the 'adult-census.csv' dataset. It contains both categorical and numerical data. Your task will be to prepare the data for processing.

  1. Read the dataset 'adult-census.csv'
  2. Explore the dataset. Carefully check which character indicates the missed data in the dataset and replace it with the np.nan object
  3. Remove rows with missing values
  4. Let's start with processing categorical data - columns 'workclass', 'sex' Use a one-hot encoding method to encode them
  5. For numeric data ('age', 'hours-per-week'), you will need to scale the data
  6. Print processed data

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

Everything was clear?

Section 6. Chapter 1
toggle bottom row

Challenge 1

Task

In this challenge, you will need to work with the 'adult-census.csv' dataset. It contains both categorical and numerical data. Your task will be to prepare the data for processing.

  1. Read the dataset 'adult-census.csv'
  2. Explore the dataset. Carefully check which character indicates the missed data in the dataset and replace it with the np.nan object
  3. Remove rows with missing values
  4. Let's start with processing categorical data - columns 'workclass', 'sex' Use a one-hot encoding method to encode them
  5. For numeric data ('age', 'hours-per-week'), you will need to scale the data
  6. Print processed data

Task

In this challenge, you will need to work with the 'adult-census.csv' dataset. It contains both categorical and numerical data. Your task will be to prepare the data for processing.

  1. Read the dataset 'adult-census.csv'
  2. Explore the dataset. Carefully check which character indicates the missed data in the dataset and replace it with the np.nan object
  3. Remove rows with missing values
  4. Let's start with processing categorical data - columns 'workclass', 'sex' Use a one-hot encoding method to encode them
  5. For numeric data ('age', 'hours-per-week'), you will need to scale the data
  6. Print processed data

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

Everything was clear?

Section 6. Chapter 1
toggle bottom row

Challenge 1

Task

In this challenge, you will need to work with the 'adult-census.csv' dataset. It contains both categorical and numerical data. Your task will be to prepare the data for processing.

  1. Read the dataset 'adult-census.csv'
  2. Explore the dataset. Carefully check which character indicates the missed data in the dataset and replace it with the np.nan object
  3. Remove rows with missing values
  4. Let's start with processing categorical data - columns 'workclass', 'sex' Use a one-hot encoding method to encode them
  5. For numeric data ('age', 'hours-per-week'), you will need to scale the data
  6. Print processed data

Task

In this challenge, you will need to work with the 'adult-census.csv' dataset. It contains both categorical and numerical data. Your task will be to prepare the data for processing.

  1. Read the dataset 'adult-census.csv'
  2. Explore the dataset. Carefully check which character indicates the missed data in the dataset and replace it with the np.nan object
  3. Remove rows with missing values
  4. Let's start with processing categorical data - columns 'workclass', 'sex' Use a one-hot encoding method to encode them
  5. For numeric data ('age', 'hours-per-week'), you will need to scale the data
  6. Print processed data

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

Everything was clear?

Task

In this challenge, you will need to work with the 'adult-census.csv' dataset. It contains both categorical and numerical data. Your task will be to prepare the data for processing.

  1. Read the dataset 'adult-census.csv'
  2. Explore the dataset. Carefully check which character indicates the missed data in the dataset and replace it with the np.nan object
  3. Remove rows with missing values
  4. Let's start with processing categorical data - columns 'workclass', 'sex' Use a one-hot encoding method to encode them
  5. For numeric data ('age', 'hours-per-week'), you will need to scale the data
  6. Print processed data

Switch to desktop for real-world practiceContinue from where you are using one of the options below
Section 6. Chapter 1
Switch to desktop for real-world practiceContinue from where you are using one of the options below
We're sorry to hear that something went wrong. What happened?
some-alt