Active Analytics

Here's an introduction and a step-by-step tutorial on using Active Analytics:

Introduction

Active Analytics is a powerful tool in Tableau Desktop that helps data analysts and scientists build and deploy models, and perform advanced analysis and prediction tasks. Active Analytics enables users to develop, test, and share models with ease, providing actionable insights from their data. With Active Analytics, users can:

• Identify trends and patterns in large datasets
• Build machine learning models to predict continuous and categorical outcomes
• Visualize and interpret complex relationships between variables
• Create dashboards to interact with predictions and trends in real-time

In this tutorial, we'll walk through the step-by-step process of using Active Analytics to analyze and predict a hypothetical sales data. Our goal is to build a machine learning model that forecasts the probability of a sales outcome (i.e., will the sale be positive or negative) based on various input variables.

Tutorial: Using Active Analytics for Predictive Modeling

Step 1: Create a New Active Analytics Model

1. Launch Tableau Desktop and sign in to your account.
2. Click on Connect to Data > Excel (or other data source) > Sales Data to load your dataset.
3. Create a new tableau worksheet and select the required data fields.
4. Go to Model > Create Model and choose Active Analytics Model.

Step 2: Prepare and Clean Your Data

1. Review your data columns and ensure they are named and formatted correctly.
2. Clean and preprocess any columns with missing or noisy values.
3. Separate your data into training, testing, and validation datasets.
4. Use Tableau's data preparation tools or custom SQL queries to enhance data quality.

Step 3: Build the Model

1. From the Active Analytics Model ribbon, select Build > Model > Binary Classification to create a machine learning model.
2. Choose a relevant algorithm (e.g., Random Forest, Log Regression, etc.) that fits your problem.
3. Select the input columns and output variable (Sale) that you want to analyze.
4. Configure additional model settings as needed, such as regularization, scoring function, and hyperparameter tuning.

Step 4: Train and Optimize the Model

1. Click Train Model to start training your model on the training data.
2. Monitor model performance and adjust hyperparameters or try different algorithms until you achieve optimal results.
3. Use Tableau's model evaluation metrics to evaluate the model's precision, recall, and other metrics.

Step 5: Deploy and Validate the Model

1. Create a new dashboard for visualizing the model predictions.
2. Use data visualizations to explore how well the model performs for different scenarios.
3. Test the model on test or validation data to check for overfitting, bias, or other model risks.

Step 6: Refine and Maintenance the Model

1. Periodically update your training data and retrain your model to account for data drift or concept drift.
2. Monitor model performance and update hyperparameters, algorithm, or data source as needed.
3. Maintain your model's fidelity by regular updates and validations to ensure accuracy and efficiency.

That's it! This tutorial has covered the basic steps of using Active Analytics to build and deploy a predictive model. Keep in mind that this is a high-level overview and more advanced topics, such as data preprocessing, data validation, and model troubleshooting, may require additional hands-on practice and experimentation with Active Analytics.