# Copy of Principal Components
When you have a large set of columns (mainly numeric), it may be interesting to reduce them to a few columns that will well represent the variability existing in the different columns.
One method for this is Core Components. Gaio uses [H2O ](http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/pca.html)to perform the calculations and summarize the data in a few columns. The algorithm accepts both numeric and categorical variables.
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## How to Use the PCA Task
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### 1. **Open the Principal Component Analysis Task**
* In the **Studio**, go to the **Tasks** panel.
* Under the **Analytics** section, select on **Principal Component Analysis**.
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### 2. **Configure the Main Fields**
* **Task label**: (optional) Name for identifying this step in your flow.
* **Result table:** Output table that will contain the principal components. Example: pca.
* **Source table:** Automatically populated with the selected table (e.g., `new_sales`).
* **Components amount:** define how many principal components you want to extract.
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### 4. **Select Columns to Remove (Optional)**
* In **Columns to remove**, you can exclude columns that should **not** be considered in the PCA calculation (e.g., IDs, codes, irrelevant fields).
* This helps avoid bias and improves the quality of the results.
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### 5. **Save and Execute**
* After setting the configuration, click **Save**.
* Run the flow — the output table will contain the extracted principal components.
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#### Output
The resulting table will include:
* The main components are presented in the first columns and then all the columns of the source table.
* One or more columns representing the **principal components** (e.g., `PCA_1`, `PCA_2`, etc.)
* A simplified dataset ready for further use in tasks like **Clustering**, **AutoML**, or 2D visualizations
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#### ✅ Best Practices
* Use PCA to:
* Reduce the number of variables in datasets with many numeric features
* Optimize performance of clustering or classification algorithms
* Simplify visualizations when working with high-dimensional data
* Combine PCA with tasks that benefit from dimensionality reduction, such as **Cluster** or **Forecast**
In this example, as 5 components were defined, five columns were created.
{% hint style="info" %}
A report is being developed that will provide a diagnosis of the components created. For now, they are only generated, but it is not possible to identify what percentage of the data variability was concentrated in each component.
{% endhint %}
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