> For the complete documentation index, see [llms.txt](https://docs.graphite-note.com/graphite-note-documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.graphite-note.com/graphite-note-documentation/demo-datasets/demo-datasets/diamonds.md).
# Diamonds
Create Multi-Class Classification model on Demo Diamonds dataset
1\. If you want to use Graphite Note demo datasets click "Import DEMO Dataset".
2\. Select the dataset you want to use to create machine learning model. In this case we will select a Diamonds dataset to create Multi Class Analysis on diamond characteristics data.
3\. Once selected, the demo dataset will load directly to your account. Dataset view will automatically open.
4\. Adjust your dataset options on Settings tab. Click Columns tab to view list of available columns with their corresponding data types. Explore dataset details on Summary tab.
5\. To create new model in the Graphite Note main menu click on "Models"
6\. You will get list of available models. Click on "New Model" to create a new one.
7\. Select a model type from our templates. In our case we will select "Multi-Class Classification" by double clicking on its name.
8\. Select the dataset you want to use to produce model. We will use "Demo-Diamonds.csv"
9\. Name your new model. We will call it "Multi-Class Classification on Demo-Diamonds"
10\. Write description of the model and select tag. If you want you can also create new tag from pop-up "Tags" window that will appear on the screen.
11\. Click "Create" to create your demo model environment.
12\. To set up a Multi Class model first you need to define "Target Feature". That is text column from your dataset you'd like to make predictions about. In case of Multi Class on Diamonds dataset target feature is "Cut" column.
13\. Click "Next" to get the list of model features that will be included under the scenario. Model relies on each column (feature) to make accurate predictions. When training the model, we will calculate which of the features are most important and behave as Key Drivers.
14\. To start training model click "Run Scenario". This will take a sample of 80% of your data and train several machine learning models.
15\. Wait for few moments and Voilà! Your Regression model is trained. Click on "Performance" tab to get model insights and view Key Drivers.
16\. Explore Multi Class model by clicking on Impact Analysis, Model Fit, Accuracy Overview or Training Results to get more insights on how model is trained and set up.
17\. If you want to take your model into action click on "Predict" tab in the main model menu.
18\. You can produce your own What-If analysis based on existing training results. You can also import a fresh CSV dataset to make predictions on target column. In our case that is "Cut". Keep in mind, the dataset you are uploading needs to contain the same feature columns as your model.
19\. Use your model often to predict future behaviour and to learn which key drivers are impacting the outcomes. The more you use and retrain your model, the smarter it becomes!
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3\. Once selected, the demo dataset will load directly to your account. Dataset view will automatically open.
4\. Adjust your dataset options on Settings tab. Click Columns tab to view list of available columns with their corresponding data types. Explore dataset details on Summary tab.
5\. To create new model in the Graphite Note main menu click on "Models"
6\. You will get list of available models. Click on "New Model" to create a new one.
7\. Select a model type from our templates. In our case we will select "Multi-Class Classification" by double clicking on its name.
8\. Select the dataset you want to use to produce model. We will use "Demo-Diamonds.csv"
9\. Name your new model. We will call it "Multi-Class Classification on Demo-Diamonds"
10\. Write description of the model and select tag. If you want you can also create new tag from pop-up "Tags" window that will appear on the screen.
11\. Click "Create" to create your demo model environment.
12\. To set up a Multi Class model first you need to define "Target Feature". That is text column from your dataset you'd like to make predictions about. In case of Multi Class on Diamonds dataset target feature is "Cut" column.
13\. Click "Next" to get the list of model features that will be included under the scenario. Model relies on each column (feature) to make accurate predictions. When training the model, we will calculate which of the features are most important and behave as Key Drivers.
14\. To start training model click "Run Scenario". This will take a sample of 80% of your data and train several machine learning models.
15\. Wait for few moments and Voilà! Your Regression model is trained. Click on "Performance" tab to get model insights and view Key Drivers.
16\. Explore Multi Class model by clicking on Impact Analysis, Model Fit, Accuracy Overview or Training Results to get more insights on how model is trained and set up.
17\. If you want to take your model into action click on "Predict" tab in the main model menu.
18\. You can produce your own What-If analysis based on existing training results. You can also import a fresh CSV dataset to make predictions on target column. In our case that is "Cut". Keep in mind, the dataset you are uploading needs to contain the same feature columns as your model.
19\. Use your model often to predict future behaviour and to learn which key drivers are impacting the outcomes. The more you use and retrain your model, the smarter it becomes!
