# Data exploration and analysis **Note** You can only use SageMaker Canvas visualizations and analytics for models built on tabular datasets. Multi-category text prediction models are also excluded. In Amazon SageMaker Canvas, you can explore the variables in your dataset using visualizations and analytics and create in-application visualizations and analytics. You can use these explorations to uncover relationships between your variables before building your model. For more information about visualization techniques in Canvas, see [Explore your data using visualization techniques](canvas-explore-data-visualization.md). For more information about analytics in Canvas, see [Explore your data using analytics](canvas-explore-data-analytics.md). # Explore your data using visualization techniques **Note** You can only use SageMaker Canvas visualizations for models built on tabular datasets. Multi-category text prediction models are also excluded. With Amazon SageMaker Canvas, you can explore and visualize your data to gain advanced insights into your data before building your ML models. You can visualize using scatter plots, bar charts, and box plots, which can help you understand your data and discover the relationships between features that could affect the model accuracy. In the **Build** tab of the SageMaker Canvas application, choose **Data visualizer** to begin creating your visualizations. You can change the visualization sample size to adjust the size of the random sample taken from your dataset. A sample size that is too large might affect the performance of your data visualizations, so we recommend that you choose an appropriate sample size. To change the sample size, use the following procedure. 1. Choose **Visualization sample**. 1. Use the slider to select your desired sample size. 1. Choose **Update** to confirm the change to your sample size. **Note** Certain visualization techniques require columns of a specific data type. For example, you can only use numeric columns for the x and y-axes of scatter plots. ## Scatter plot To create a scatter plot with your dataset, choose **Scatter plot** in the **Visualization** panel. Choose the features you want to plot on the x and y-axes from the **Columns** section. You can drag and drop the columns onto the axes or, once an axis has been dropped, you can choose a column from the list of supported columns. You can use **Color by** to color the data points on the plot with a third feature. You can also use **Group by** to group the data into separate plots based on a fourth feature. The following image shows a scatter plot that uses **Color by** and **Group by**. In this example, each data point is colored by the `MaritalStatus` feature, and grouping by the `Department` feature results in a scatter plot for the data points of each department. ![\[Screenshot of a scatter plot in the Data visualizer view of the Canvas application.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/studio/canvas/canvas-eda-scatter-plot.png) ## Bar chart To create a bar chart with your dataset, choose **Bar chart** in the **Visualization** panel. Choose the features you want to plot on the x and y-axes from the **Columns** section. You can drag and drop the columns onto the axes or, once an axis has been dropped, you can choose a column from the list of supported columns. You can use **Group by** to group the bar chart by a third feature. You can use **Stack by** to vertically shade each bar based on the unique values of a fourth feature. The following image shows a bar chart that uses **Group by** and **Stack by**. In this example, the bar chart is grouped by the `MaritalStatus` feature and stacked by the `JobLevel` feature. For each `JobRole` on the x axis, there is a separate bar for the unique categories in the `MaritalStatus` feature, and every bar is vertically stacked by the `JobLevel` feature. ![\[Screenshot of a bar chart in the Data visualizer view of the Canvas application.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/studio/canvas/canvas-eda-bar-chart.png) ## Box plot To create a box plot with your dataset, choose **Box plot** in the **Visualization** panel. Choose the features you want to plot on the x and y-axes from the **Columns** section. You can drag and drop the columns onto the axes or, once an axis has been dropped, you can choose a column from the list of supported columns. You can use **Group by** to group the box plots by a third feature. The following image shows a box plot that uses **Group by**. In this example, the x and y-axes show `JobLevel` and `JobSatisfaction`, respectively, and the colored box plots are grouped by the `Department` feature. ![\[Screenshot of a box plot in the Data visualizer view of the Canvas application.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/studio/canvas/canvas-eda-box-plot.png) # Explore your data using analytics **Note** You can only use SageMaker Canvas analytics for models built on tabular datasets. Multi-category text prediction models are also excluded. With analytics in Amazon SageMaker Canvas, you can explore your dataset and gain insight on all of your variables before building a model. You can determine the relationships between features in your dataset using correlation matrices. You can use this technique to summarize your dataset into a matrix that shows the correlations between two or more values. This helps you identify and visualize patterns in a given dataset for advanced data analysis. The matrix shows the correlation between each feature as positive, negative, or neutral. You might want to include features that have a high correlation with each other when building your model. Features that have little to no correlation might be irrelevant to your model, and you can drop those features when building your model. To get started with correlation matrices in SageMaker Canvas, see the following section. ## Create a correlation matrix You can create a correlation matrix when you are preparing to build a model in the **Build** tab of the SageMaker Canvas application. For instructions on how to begin creating a model, see [Build a model](canvas-build-model-how-to.md). After you’ve started preparing a model in the SageMaker Canvas application, do the following: 1. In the **Build** tab, choose **Data visualizer**. 1. Choose **Analytics**. 1. Choose **Correlation matrix**. You should see a visualization similar to the following screenshot, which shows up to 15 columns of the dataset organized into a correlation matrix. ![\[Screenshot of a correlation matrix in the Canvas application.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/studio/canvas/canvas-correlation-matrix-2.png) After you’ve created the correlation matrix, you can customize it by doing the following: ### 1. Choose your columns For **Columns**, you can select the columns that you want to include in the matrix. You can compare up to 15 columns from your dataset. **Note** You can use numeric, categorical, or binary column types for a correlation matrix. The correlation matrix doesn’t support datetime or text data column types. To add or remove columns from the correlation matrix, select and deselect columns from the **Columns** panel. You can also drag and drop columns from the panel directly onto the matrix. If your dataset has a lot of columns, you can search for the columns you want in the **Search columns** bar. To filter the columns by data type, choose the dropdown list and select **All**, **Numeric**, or **Categorical**. Selecting **All** shows you all of the columns from your dataset, whereas the **Numeric** and **Categorical** filters only show you the numeric or categorical columns in your dataset. Note that binary column types are included in the numeric or categorical filters. For the best data insights, include your target column in the correlation matrix. When you include your target column in the correlation matrix, it appears as the last feature on the matrix with a target symbol. ### 2. Choose your correlation type SageMaker Canvas supports different *correlation types*, or methods for calculating the correlation between your columns. To change the correlation type, use the **Columns** filter mentioned in the preceding section to filter for your desired column type and columns. You should see the **Correlation type** in the side panel. For numeric comparisons, you have the option to select either **Pearson** or **Spearman**. For categorical comparisons, the correlation type is set as **MI**. For categorical and mixed comparisons, the correlation type is set as **Spearman & MI**. For matrices that only compare numeric columns, the correlation type is either Pearson or Spearman. The Pearson measure evaluates the linear relationship between two continuous variables. The Spearman measure evaluates the monotonic relationship between two variables. For both Pearson and Spearman, the scale of correlation ranges from -1 to 1, with either end of the scale indicating a perfect correlation (a direct 1:1 relationship) and 0 indicating no correlation. You might want to select Pearson if your data has more linear relationships (as revealed by a [scatter plot visualization](https://docs.aws.amazon.com/sagemaker/latest/dg/canvas-explore-data.html#canvas-explore-data-scatterplot)). If your data is not linear, or contains a mixture of linear and monotonic relationships, then you might want to select Spearman. For matrices that only compare categorical columns, the correlation type is set to Mutual Information Classification (MI). The MI value is a measure of the mutual dependence between two random variables. The MI measure is on a scale of 0 to 1, with 0 indicating no correlation and 1 indicating a perfect correlation. For matrices that compare a mix of numeric and categorical columns, the correlation type **Spearman & MI** is a combination of the Spearman and MI correlation types. For correlations between two numeric columns, the matrix shows the Spearman value. For correlations between a numeric and categorical column or two categorical columns, the matrix shows the MI value. Lastly, remember that correlation does not necessarily indicate causation. A strong correlation value only indicates that there is a relationship between two variables, but the variables might not have a causal relationship. Carefully review your columns of interest to avoid bias when building your model. ### 3. Filter your correlations In the side panel, you can use the **Filter correlations** feature to filter for the range of correlation values that you want to include in the matrix. For example, if you want to filter for features that only have positive or neutral correlation, you can set the **Min** to 0 and the **Max** to 1 (valid values are -1 to 1). For Spearman and Pearson comparisons, you can set the **Filter correlations** range anywhere from -1 to 1, with 0 meaning that there is no correlation. -1 and 1 mean that the variables have a strong negative or positive correlation, respectively. For MI comparisons, the correlation range only goes from 0 to 1, with 0 meaning that there is no correlation and 1 meaning that the variables have a strong correlation, either positive or negative. Each feature has a perfect correlation (1) with itself. Therefore, you might notice that the top row of the correlation matrix is always 1. If you want to exclude these values, you can use the filter to set the **Max** less than 1. Keep in mind that if your matrix compares a mix of numeric and categorical columns and uses the **Spearman & MI** correlation type, then the *categorical x numeric* and *categorical x categorical* correlations (which use the MI measure) are on a scale of 0 to 1, whereas the *numeric x numeric* correlations (which use the Spearman measure) are on a scale of -1 to 1. Review your correlations of interest carefully to ensure that you know the correlation type being used to calculate each value. ### 4. Choose the visualization method In the side panel, you can use **Visualize by** to change the visualization method of the matrix. Choose the **Numeric** visualization method to show the correlation (Pearson, Spearman, or MI) value, or choose the **Size** visualization method to visualize the correlation with differently sized and colored dots. If you choose **Size**, you can hover over a specific dot on the matrix to see the actual correlation value. ### 5. Choose a color palette In the side panel, you can use **Color selection** to change the color palette used for the scale of negative to positive correlation in the matrix. Select one of the alternative color palettes to change the colors used in the matrix.