Turn Data into Worthy Predictions with Tableau

Predictive analysis: It is a type of data analysis which uses statistical and machine learning techniques to make predictions about the future events or consequences based on historical data. In Tableau, you can use a variety of tools and techniques to perform predictive analysis, including linear regression, decision trees, and forecasting.

Some examples of predictive analysis in Tableau include:

• Clustering: Clustering is a technique used to group similar data points together. Tableau supports clustering through integration with R and Python, and also provides built-in clustering capabilities.
• Classification: Classification is a technique used to predict the class or category of a data point based on the values of other variables. Tableau supports classification through integration with R and Python, and also provides built-in classification capabilities.
• Regression: Regression is a technique used to predict a continuous variable based on the values of other variables. Tableau supports regression through integration with R and Python, and also provides built-in regression capabilities.
• Decision Trees: Decision Trees are a popular predictive modeling technique. Tableau supports Decision Trees through integration with R and Python.
• Neural Networks: These are powerful machine learning technique which can be used for predictive modeling. Tableau supports neural networks through integration with R and Python.

Here are few steps that you can follow for performing predictive analysis in Tableau:

1. Connect to your data source in Tableau.
2. Prepare your data for analysis by cleaning and transforming it as needed.
3. Select the appropriate predictive analysis technique based on your data and the type of prediction you want to make.
4. Use the “Regression” option in the “Analysis” menu to create a linear regression model.
5. Use the “Decision Tree” option in the “Analysis” menu to create a decision tree model.
6. Use the “Forecast” option in the “Analysis” menu to create a forecast.
7. Use the “Cluster” option in the “Analysis” menu to create a cluster analysis.
8. Visualize and interpret your results to understand the predictions and insights that can be gained from your data.

Keep in mind that predictive analysis can be complex and requires a strong understanding of statistical and machine learning techniques. It is also important for us to carefully consider the limitations and potential biases in your data when performing predictive analysis.