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Create Structed Data Category Classification Model

1. What is Structed Data Category Classification?

Category classification models are models that first learn from labeled learning data and then predict which classes the newly entered data belongs to. Classification belongs to the supervised learning category where the target comes with the input data. There are many applications for classification in many areas, such as credit approval, medical diagnosis, target marketing, etc.

To classify categories by combining two or more data, you can find more information in Combining Data.

Try it yourself

Learning Objective: To predict satisfaction by utilizing customer aviation data

1. View Data

Data: flight_service.csv

Data features consist of 21 characteristics, ranging from basic personal information such as customer gender, customer type, and me, to data such as travel purpose, travel time, and in-flight service, which affect aviation satisfaction.

The resulting deposit results are divided into 'neutral or dissatisfied' and 'satisfied'.

2. Create an AI Model

1) Upload data


1-1) Click DS2 DATASET for data upload.

1-2) Click Add Data to add data for artificial intelligence development.

1-3) Because downloaded data is a .csv file, click CSV and click Next.

1-4) Click the Find File button to click and upload the data you want to download.

1-5) You can check the uploaded file and select the result column in the data settings.

1-6) Data is being uploaded.

2) Develop artificial intelligence


2-1) Select your training data to create AI model at DS2 DATASET and click START AI button .


Please choose the learning type and preferred method for the project.

2-2) Learning type - Select 'Structed Data Category Classification' for the learning type.

2-3) Analysis Criteria - The preferred method is divided into two, and I'll choose the 'high accuracy' method.

2-4) Analysis Units - Click 'satisfication - flight_service.csv' for the value you want to analyze/predict.

Learning usage

2-5) At the bottom, you can choose whether to use learning data or not along with data summarization. (Values that you want to analyze/predict will automatically be disabled.)


2-6) Preprocess Data → You can preprocess by selecting the value you want to preprocess.
- If necessary, click the preprocess button to perform the preprocessing.

2-7) Select the desired preprocessing features and click Finish.

2-8) Completed preprocessing can be verified by marking completion.

2-9) After all the work is done, click Start in the top right to create artificial intelligence.

2-10) creating artificial intelligence.

3. View/Predict AI Details

CLICK AI can select the optimal AI model by generating multiple AI models and checking the model-specific details. Once all artificial intelligence models are created, the prediction accuracy of each model can be checked and compared, and detailed views, individual predictions, and batch predictions can be made for each model.

1) Detail


1-1) Model performance evaluation

In Detail, you can check the accuracy and error rate of the model.

1-2) Feature Importance

Feature Importance is the importance of a variable, indicating how much influence each variable has on its predictions.

1-3) API

API provides API to utilize the model as a programming language. CLICK AI's API supports JavaScript, Python, Wget, and Java languages.

1-4) Sharing a service app

If you click the "Share service app" button to the right of "TRAINING MODEL (model number)", you can predict and analyze outside of DS2.AI.

2) Single prediction


If you select Single Prediction, you can get one prediction value as a result by entering the value of each variable. It can be used to test the accuracy of a single prediction or model. You can check the result by entering data that matches the key.

3) Collective prediction


3-1) If you select Batch Prediction, you can predict a lot of data at once. Click Download Predictive Template to download the template. If the letters in the downloaded csv file are broken, please refer to the encoding conversion.

3-2) Populate the data into the template you downloaded.

3-3) Upload the predictive template that filled the data, then click Next.

3-4) Once the batch prediction is complete, you can check it through the notification window.

4. Take advantage of artificial intelligence

It can be used as a form of analysis, download, deployment, and sale for each AI model generated.

1) Analyze


  • It can be used as a form of analysis, download, deployment, and sale for each AI model generated.

2) Download


  • You can purchase a model license by clicking Download. When purchasing a model license, email the Deep Learning model file with code that enables Inference functionality on Jupyter.

*If you purchase Jetson Nano 2GB Developer Kit's chipset separately, you can use the artificial intelligence model through embedded products without connecting to the operating server.

3) Deploy


  • When you choose a deployment, you rent a cloud server to provide integrated MLops to distribute, operate, and manage that model.
  • Select the cloud provider and region you want to see the list of available instances.
  • Select the desired instance and click "CREATE CLUSTER" at the bottom to take advantage of AIOps linked to [SKYHUB AI | Deploy].

4) Selling


  • If you choose to sell, you can sell the generated model to the AI Market of DS2.AI. Please select the desired price and sales option of the model and click on the sales request. It will be uploaded to AI Market after reviewing the adequacy of AI model and data security procedures.

  • Models that are determined to be available for sale can be found on the AI Market's product list, and if the customer's model is purchased by other users who need it, it generates a set amount of revenue (20% commission).