> 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/ads.md).
# Ads
Create a Regression model on Demo Ads 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 a machine learning model. In this case we will select "Ads" dataset to create a Regression Analysis on marketing ads data.
3\. Once selected, the demo dataset will load directly to your account. The dataset view will automatically open.
4\. Adjust your dataset options on the 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 a 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 model type from our templates. In our case, we will select "Regression" by double clicking on its name.
8\. Select dataset you want to use to produce a model. We will use "Demo-Ads.csv."
9\. Name your new model. We will call it "Regression on Demo-Ads".
10\. Write the description of the model and select tag. If you want you can also create a 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 "Regression Model", first you will need to define the "Target Feature". That is a numeric column from your dataset that you'd like to make predictions about. In the case of Regression on Ads dataset, target feature is "Clicks" column.
13\. Click "Next" to get the list of model features that will be included in the scenario. Model relies upon 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 the 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 the Regression Model by clicking on Impact Analysis, Model Fit and Training Results to get more insights on how the model is trained and set up.
17\. If you want to take turn 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 into the data model, to make predictions on target column. In our case that is "Clicks". Keep in mind, the dataset you are uploading needs to contain 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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---
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## Querying This Documentation
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3\. Once selected, the demo dataset will load directly to your account. The dataset view will automatically open.
4\. Adjust your dataset options on the 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 a 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 model type from our templates. In our case, we will select "Regression" by double clicking on its name.
8\. Select dataset you want to use to produce a model. We will use "Demo-Ads.csv."
9\. Name your new model. We will call it "Regression on Demo-Ads".
10\. Write the description of the model and select tag. If you want you can also create a 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 "Regression Model", first you will need to define the "Target Feature". That is a numeric column from your dataset that you'd like to make predictions about. In the case of Regression on Ads dataset, target feature is "Clicks" column.
13\. Click "Next" to get the list of model features that will be included in the scenario. Model relies upon 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 the 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 the Regression Model by clicking on Impact Analysis, Model Fit and Training Results to get more insights on how the model is trained and set up.
17\. If you want to take turn 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 into the data model, to make predictions on target column. In our case that is "Clicks". Keep in mind, the dataset you are uploading needs to contain 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!
\