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🤖 Get Started with AI Builder Prediction

📅 Published Date: March 7, 2025    ✍ Author: Pranay Reddy Muthyala

📌 Introduction

Making data-driven decisions is critical for businesses, and AI Builder’s Prediction model enables organizations to forecast outcomes based on historical data.

AI Builder’s Prediction model is part of Microsoft Power Platform, allowing businesses to predict future outcomes using patterns identified in historical data. This helps in automating processes, reducing risks, and making more informed decisions.

🧠 Understanding AI Builder’s Prediction Models

AI Builder’s Prediction model uses historical data to forecast outcomes, helping businesses automate decision-making and optimize operations.

🔹 How AI Builder Prediction Models Work

✔ Binary Predictions – When the outcome is yes/no, true/false (e.g., “Will a customer churn?”).
✔ Multiple Category Predictions – When there are three or more possible outcomes (e.g., “What product category will a customer buy next?”).

🔹 Data Requirements for Training a Prediction Model

✔ Outcome Column – Represents the final decision (e.g., “Subscription Renewed: Yes/No”).
✔ At Least 1000 Historical Records – AI Builder recommends 1000+ records for higher accuracy.
✔ Consistent Data Structure – Ensure input values align across records.
✔ No Missing Data in Outcome Column – AI Builder cannot train on empty values.

🛠️ Steps to Build & Train a Prediction Model in AI Builder

1️⃣ Create a New Prediction Model

• Sign in to Power Apps or Power Automate.
• Navigate to AI Builder > Models and click + New Prediction Model.

2️⃣ Select a Dataset

• Choose a Dataverse table with historical data.
• Ensure the dataset contains:
  ✔ A clear outcome column (e.g., “Customer Churn: Yes/No”).
  ✔ Relevant input fields (e.g., “Last Purchase Date,” “Customer Complaints”).
  ✔ At least 1000+ records for accuracy.

3️⃣ Define the Outcome to Predict

• Select the column that represents the final decision or category the model should predict.
• AI Builder will analyze patterns and trends in historical data to generate forecasts.

4️⃣ Select Data Columns for Training

• AI Builder will automatically select data columns that it believes are most relevant for making predictions.
• You can review and adjust the selection by adding or removing columns.
• Ensure that only meaningful data fields are included to improve accuracy.
• Remove columns that contain irrelevant or unique values (e.g., IDs, timestamps) since they do not contribute to the prediction.

5️⃣ Train the Prediction Model

• Click Train Model, and AI Builder will analyze relationships in the dataset.
• AI Builder identifies patterns between input data and the outcome column to make predictions.
• The model then tests itself against historical data to evaluate accuracy before deployment.
• Once training is complete, AI Builder scores the model’s performance, which is reviewed in the next step.

6️⃣ Review Model Accuracy & Improve Predictions

• After training, AI Builder provides a performance grade to help evaluate accuracy:
  • Grade A – The best grade; improvement might still be possible.
  • Grade B – Prediction is correct for most cases; improvement can be considered.
  • Grade C – Better than a random guess, but adjustments are recommended.
  • Grade D – Similar or worse than a random guess or near 100% accuracy. The model and dataset must be revised.
• If accuracy is low:
  ✔ Improve dataset quality (remove irrelevant fields or missing data).
  ✔ Add more historical records to help AI recognize patterns better.

7️⃣ Publish & Deploy the Model

• Once satisfied with accuracy, publish the model.
• The model is now ready to be used in:
  ✅ Power Automate – Automate decision-making workflows.
  ✅ Power Apps – Integrate AI predictions into apps.
  ✅ Dataverse – Store and analyze predicted outcomes.

📊 Benefits of AI Builder’s Prediction Model

✔ Data-Driven Decision Making – Predict customer churn, sales trends, and more.
✔ Seamless Integration – Works with Power Automate, Power Apps, and Dataverse.
✔ Automates Business Processes – Reduce manual work and improve efficiency.

By leveraging AI Builder’s Capabilities businesses can make smarter, data-driven decisions! 🚀

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