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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

bookChallenge 2

Task

So, now let's remember what we do with text data. You will be working with the 'reviews.csv' dataset, and your task will be to encode text data using the bag of words method.

  1. Create a CountVectorizer object to tokenize text
  2. Fit data to the tokenizer
  3. Print the vocabulary of the tokenizer
  4. Encode 'Text' column
  5. Print the results

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Section 6. Chapter 2
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bookChallenge 2

Task

So, now let's remember what we do with text data. You will be working with the 'reviews.csv' dataset, and your task will be to encode text data using the bag of words method.

  1. Create a CountVectorizer object to tokenize text
  2. Fit data to the tokenizer
  3. Print the vocabulary of the tokenizer
  4. Encode 'Text' column
  5. Print the results

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 6. Chapter 2
toggle bottom row

bookChallenge 2

Task

So, now let's remember what we do with text data. You will be working with the 'reviews.csv' dataset, and your task will be to encode text data using the bag of words method.

  1. Create a CountVectorizer object to tokenize text
  2. Fit data to the tokenizer
  3. Print the vocabulary of the tokenizer
  4. Encode 'Text' column
  5. Print the results

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Task

So, now let's remember what we do with text data. You will be working with the 'reviews.csv' dataset, and your task will be to encode text data using the bag of words method.

  1. Create a CountVectorizer object to tokenize text
  2. Fit data to the tokenizer
  3. Print the vocabulary of the tokenizer
  4. Encode 'Text' column
  5. Print the results

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 6. Chapter 2
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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