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Preparing Predictive Analysis Data

Updated on 2024-12-31 GMT+08:00

Before using ModelArts to build a predictive analytics model, upload data to OBS. The OBS bucket and ModelArts must be in the same region. For example, if the OBS bucket is in the CN North-Beijing4 region, ensure that the ModelArts management console is also in the CN North-Beijing4 region. Otherwise, data cannot be obtained.

Requirements on Datasets

The data set used in the predictive analytics project must be a table dataset in .csv format. For details about the table dataset, see Table dataset.

NOTE:

To convert the data from .xlsx to .csv, perform the following operations:

Save the original table data in .xlsx. Choose File > Save As, select a local address, set Save as type: to CSV (Comma delimited), and click Save. Then, click OK in the displayed dialog box.

Requirements on the training data:
  • The number of columns in the training data must be the same, and there has to be at least 100 data records (a feature with different values is considered as different data records).
  • The training columns cannot contain timestamp data (such as yy-mm-dd or yyyy-mm-dd).
  • If a column has only one value, the column is considered invalid. Ensure that there are at least two values in the label column and no data is missing.
    NOTE:

    The label column is the training target specified in a training task. It is the output (prediction item) for the model trained using the dataset.

  • In addition to the label column, the dataset must contain at least two valid feature columns. Ensure that there are at least two values in each feature column and that the percentage of missing data must be lower than 10%.
  • Due to the limitation of the feature filtering algorithm, place the predictive data column at the last. Otherwise, the training may fail.

Example of a table dataset:

The following table takes the bank deposit predictive dataset as an example. Data sources include age, occupation, marital status, cultural level, and whether there is a personal mortgage or personal loan.

Table 1 Fields and meanings of data sources

Field

Meaning

Type

Description

attr_1

Age

Int

Age of the customer

attr_2

Occupation

String

Occupation of the customer

attr_3

Marital status

String

Marital status of the customer

attr_4

Education status

String

Education status of the customer

attr_5

Real estate

String

Housing situation of the customer

attr_6

Loan

String

Loan of the customer

attr_7

Deposit

String

Deposit of the customer

Table 2 Sample data of the dataset

attr_1

attr_2

attr_3

attr_4

attr_5

attr_6

attr_7

31

blue-collar

married

secondary

yes

no

no

41

management

married

tertiary

yes

yes

no

38

technician

single

secondary

yes

no

no

39

technician

single

secondary

yes

no

yes

39

blue-collar

married

secondary

yes

no

no

39

services

single

unknown

yes

no

no

Uploading Data to OBS

In this section, the OBS console is used to upload data.

Upload files to OBS according to the following specifications:

The OBS path of the predictive analytics projects must comply with the following rules:

  • The OBS path of the input data must redirect to the data files. The data files must be stored in a folder in an OBS bucket rather than the root directory of the OBS bucket, for example, /obs-xxx/data/input.csv.
  • There must be at least 100 lines of valid data in .csv. There cannot be more than 200 columns of data and the total data size must be smaller than 100 MB.

Procedure for uploading data to OBS:

Perform the following operations to import data to the dataset for model training and building.

  1. Log in to the OBS console and create a bucket in the same region as ModelArts. If an available bucket exists, ensure that the OBS bucket and ModelArts are in the same region.
  2. Upload the local data to the OBS bucket. If you have a large amount of data, use OBS Browser+ to upload data or folders. The uploaded data must meet the dataset requirements of the ExeML project.
NOTE:

Upload data from unencrypted buckets. Otherwise, training will fail because data cannot be decrypted.

Creating a Dataset

After data is prepared, create a dataset of the type supported by the project. For details, see Creating a Dataset.

FAQs

How do I process Schema information when creating a table dataset using data selected from OBS?

Schema information includes the names and types of table columns, which must be the same as those of the imported data.
  • If the original table contains a table header, enable Contain Table Header. The first row's data of the file will be used as column names. You do not need to modify the Schema information.
  • If the original table does not contain a table header, disable Contain Table Header. After data is selected from OBS, the first row's data of the table will be used as the column names by default. Change the column names in Schema information to attr_1, attr_2, ..., attr_n. attr_n is the prediction column placed at last.

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