The document discusses various techniques for preparing data in DataRobot, including importing datasets, removing irrelevant features, exploring datasets to create profiles, engineering data through projects by exploring values, splitting columns, changing types, and using regular expressions and computed functions to clean and transform data. It also covers publishing datasets to libraries, exporting datasets, and creating baselines.

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2. PREPARING THE FIFA DATASET
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3. DATA PREPARATION USING DATA PREP (PAXATA)
 Import the dataset from the local file system or some data source
 Rename the dataset and provide tags to the imported dataset
 Remove features that are not relevant to model development
 Baseline the dataset by creating a version through saving the dataset
 Explore the dataset through creating a dataset profile, which will be stored as a separate file
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13. WHAT NOT TO DO
 DataRobot will take care of performing featurization tasks
 Therefore, you do not need to perform the following
• Categorical variables encoding
• One-hot encoding
• Missing value imputation
 Dataset Profiles do not provide exactly the same statistics provided by pandas (e.g. unique, value counts, describe)
• Feature statistics are however provided within the Project page (e.g. using FILTER values)
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14. BASIC DATA PRE-PROCESSING
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15. DATA ENGINEERING THROUGH ESTABLISHING A PROJECT
 Create a project and select the imported dataset
 Explore the features using "FILTER values"
 Show value counts using "FILTER values"
 Split a value into two values using "COLUMN split" and separator string matching
 Remove leading space using "WHITESPACE trim leading and trailing"
 Remove text from a column that should be numeric only using "COLUMN find + replace"
 Change the type of the cleaned column using "CHANGE into ..." and "numeric"
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32. PATTERN-BASED DATA PRE-PROCESSING
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33. DATA ENGINEERING USING REGULAR EXPRESSION
 Extract data using "COLUMN split", regular expression, and capture mode
 Comprehend the specification for number, character, and repetition in regular expression
 Comprehend the use of brackets to extract data using a regular expression
 Remove redundant feature columns using "COLUMNS"
 Understand the impact of values with different units
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([0-9]{1,3})[+][0-9]
part to be captured & output
number of repetition

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€([0-9.]{1,6})([MK])
part to be captured & output
number of
repetition

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([0-9])'([0-9]{1,2})
part to be captured & output
number of
repetition

44. FUNCTION-BASED DATA PRE-PROCESSING
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45. DATA ENGINEERING USING COMPUTED FUNCTION
 Create a new column using simple computation over other columns
 Create a new column using multi-layer conditions over other columns
 Cross-examine conditions against computed values using "FILTER values"
 Cross-examine conditions against computed values using the list display of "FILTER values"
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Height in FT = @Height ft@ + @Height in@ / 12

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Release Final = IF(@Release Unit@ = "M", 1000000 * @Release Amt@, IF(@Release Unit@ = "K", 1000 * @Release Amt@, 0))

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Value Final = IF(@Value Unit@ = "M", 1000000 * @Value Amt@, IF(@Value Unit@ = "K", 1000 * @Value Amt@, 0))

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Wage Final = IF(@Wage Unit@ = "M", 1000000 * @Wage Amt@, IF(@Wage Unit@ = "K", 1000 * @Wage Amt@, 0))

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52. DATA BASELINING
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53. PUBLISHING & EXPORTING DATASET
 Publish current dataset as an AnswerSet to the Library for shared access
 Generate a dataset profile using the newly created AnswerSet
 Export the current dataset to the file system
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