To be able to create predictions, you must enable them first. ## Enabling Lookalikes --- The [Lookalikes](/docs/glossary#lookalikes) model allows you to compare two groups (*source* and *target*) and search the target group for customers whose behavior is predicted to be similar to those from the source group. **Prerequisites:** - [Configure AI engine](/docs/settings/configuration/ai-engine-configuration/engine-configuration-for-propensity) - Transaction events must exist in the system. To enable this prediction model: 1. Go to **Settings > AI Engine Configuration**. 2. Click the **Predictions** tab. 3. In the **Lookalikes** section, click **Define**. 4. Switch the toggle to **Enabled**. 5. In the **Events** section, select at least one event which the model will use for the training and for scoring the customers.

Configuration of the Lookalikes model in the item feed

6. Confirm the settings by clicking **Apply**. After enabling the Lookalikes module, you must wait 2-3 hours for the model to become ready. After that, you can create the first [lookalike prediction](/docs/ai-hub/predictions/lookalikes).

You can get inspired by uses cases with the Lookalikes model: - [Find customers for a new offline shop branch](/use-cases/discover-customers-for-new-shop-branch) - [Find best matching customers for an annual campaign](/use-cases/find-matching-customers-for-campaign)

## Enabling Regression and Classification Predictions ---

Previously referred to as Custom predictions, regression and classification predictions are the current names for the same concept.

The Custom model allows you to create two types of predictions, which differ in terms of the result types. - You can create a prediction that [produces a numerical value](/docs/glossary#regression), it works best with forecasting customer LTV or the number of purchased products. - The other type [produces true/false values](/docs/glossary#classification), which is best used to answer yes/no questions, such as "will this customer leave next month?" To enable this prediction model: 1. Go to **Settings > AI Engine Configuration**. 2. Click the **Predictions** tab. 3. In the **Custom** section, click **Define**. 4. Switch the toggle to **Enabled**. 5. In the **Time shift** section, define three time ranges for later selection in the [settings](/docs/ai-hub/predictions/custom#schedule-recalculation-and-result-settings) of a single prediction as the **How many days in advance do you want to make a prediction?** option.

Editing time ranges will be impossible after you click **Apply**.

6. Optionally, in the **Custom events** section, you can add custom events which will be analyzed by the model while calculating the results of a prediction. 7. Confirm the settings by clicking **Apply**.

Configuration of the Custom model in the item feed

After enabling the Custom model, you must wait 2-3 hours for the model to become ready. After that, you can create the first [custom prediction](/docs/ai-hub/predictions/custom).

You can get inspired by uses cases with the custom model: - [Lifetime value prediction](/use-cases/ltv-prediction) - [Predict churn](/use-cases/churn-prediction) - [Evaluate results of churn predictions](/use-cases/predictions-dashboard)

## Enabling Propensity and Best fit predictions --- Propensity and Best Fit models can't be enabled separately. To enable them, see [the instructions](/docs/settings/configuration/ai-engine-configuration/engine-configuration-for-propensity). After enabling the Propensity and Best Fit module, you must wait 2-3 hours for the model to become ready. After that, you can create the first [propensity](/docs/ai-hub/predictions/propensity) and [best fit](/docs/ai-hub/predictions/bestfit) predictions.

You can get inspired by uses cases with the propensity model: - [Predict propensity to buy items with an attribute](/use-cases/propensity-attribute) - [Predict propensity to buy an item](/use-cases/propensity-product) - [Predict propensity to buy items from specific brands](/use-cases/propensity-brand)