Preparing Data

Before using ModelArts to build a predictive analytics model, upload data to OBS. The OBS bucket and ModelArts must be in the same region.

Uploading Data to OBS

This operation uses the OBS console to upload data.

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

Log in to 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.
Upload a file 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.

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

Requirements on Datasets

The name of a file in a dataset consists of letters, digits, hyphens (-), and underscores (_), and the file name extension is CSV. The files cannot be stored in the root directory of an OBS bucket, but in a folder in the OBS bucket, for example, /obs-xxx/data/input.csv.
The files are saved in CSV format. Use newline characters (\n) to separate lines and commas (,) to separate columns in the file. The file content cannot contain Chinese characters. The column content cannot contain special characters such as commas (,) and newline characters (\n). The quotation marks are not supported. It is recommended that the column content consist of letters and digits.
Data training
- The number of training columns is the same. There are at least 100 different data records in total (a feature with different values is considered as different data).
- 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.
  
  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%.
- The training data in CSV file cannot contain the table header. Otherwise, the training fails.
- Due to the limitation of the feature filtering algorithm, place the label column in the last column of the dataset. Otherwise, the training may fail.

Requirements for Files Uploaded to OBS

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.
The input data must be in CSV format. The data files do not contain the table header and the number of valid data lines must be greater than 100. The number of columns must be less than 200, and the total data size cannot exceed 100 MB.

Predictive Analytics File Example

Take the iris dataset as an example. Predict an iris species based on the lengths and widths of the iris calyx and petal.

**Table 1** Parameters and meanings of data sources
Parameter	Meaning	Type	Description
attr_1	Calyx length	Double	Length of the target iris calyx
attr_2	Calyx width	Double	Width of the target calyx
attr_3	Petal length	Double	Length of the target iris petal
attr_4	Petal width	Double	Width of the target iris petal
attr_5	Species	String	Species of the iris