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Combine multiple training data

1. Combine multiple training data with DS2 Dataset

When we have all the information we want in one data file, it's the best situation for AI learning. However, when one data file lacks information or requires additional data, we must combine more than one data for artificial intelligence with higher reliability or accuracy. Generally, it can be done using programming or via Excel, but in the case of programming, there is a premise that professional knowledge is required, and Excel-based work is time-consuming labor.

DS2 Dataset provides an easy way to combine data with just a click instead of the two mentioned methods, and data combination applies only to text-based artificial intelligence models.

2. Merge Multiple Datasets

Learning goals : AI training after data combination using two datasets

Learning materials : glass refractive index.csv, glass components.csv
glass refractive index.csv
glass element.csv

If you look at the two datasets above, they have different data.

1)glass refractive index.csv

Forglass refractive index, the components are organized for each glass with an id value.

2)glass element.csv

glass element..csv, for each glass with an id value, the refractive index and the type of glass are categorized and represented by numbers from 1 to 7.

In order to distinguish types of glass, not only the refractive index of glass, but also the components of glass play an important role in distinguishing types of glass. Therefore, it is necessary to combine the above two data.

1-1. Upload Data

Upload the glass refractive index.csv file and the glass component.csv file. You are able to refer to Upload training data pageto upload data.

1-2. Merge data


1) Click on the two uploaded data, and click Start AI Development.

1-3. Develop artificial intelligence


1) Since it is to predict the types of glass divided by categories, the learning type is to select a structured data category classification.

2) Preferred methods are divided into two, and I will choose 'high accuracy' method.

3) Select 'Glass type - Glass refractive index.csv' because the final value you want to predict is the type of glass in the file Glass refractive index.csv.

1-4.Linking data


1) Once you have selected the value you want to analyze/predict, an interlock is created.

2) Click id for each dataset because the data that both data have in common is an id value.

3) The interlocking is complete! You can now create an artificial intelligence model by clicking the START button in the upper right corner.

4) When you click the START button, the model creation begins and you can check the progress of the model creation.

When you click the START button, the model creation begins and you can check the progress of the model creation.