ai in power bi automated machine learning model in power bi

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sure! below is an informative tutorial on how to use automated machine learning (automl) in power bi, including a code example. this tutorial will guide you through the process of building a predictive model using power bi's ai capabilities.

tutorial: using automated machine learning in power bi

prerequisites

1. **power bi account**: ensure you have a power bi pro or premium account.
2. **data**: you need to have a dataset loaded into power bi desktop or power bi service. for this tutorial, we’ll assume you have a dataset of customer data, including features like age, income, and whether they churned (binary outcome).

step 1: prepare your data

1. **load data**: load your dataset into power bi. you can use data from excel, sql server, or any other source supported by power bi.
2. **data cleaning**: clean your data to handle missing values, remove duplicates, and ensure all features are in the right format.

step 2: access automated ml

1. **navigate to power bi service**: after preparing your data in power bi desktop, publish it to power bi service.
2. **open the dataset**: in power bi service, go to the dataset you published.
3. **create a new ml model**:
click on the ellipsis (...) next to your dataset.
select "create" and then choose "automl".

step 3: set up the automl experiment

1. **choose a task**: select the type of machine learning task you want to perform (e.g., classification or regression). for example, if you want to predict customer churn, you would select classification.
2. **select target column**: choose the target column, which is the outcome you want to predict (e.g., `churn`).
3. **configure input columns**: select the columns you want to use as features (e.g., `age`, `income`, etc.).
4. **training settings**: configure settings such as experiment name and the amount of compute resources.

step 4: run the experiment

1. **start the experiment**: click on the "run" button to start the automl experiment. power bi will automatic ...

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