How to build a predictive model

6 steps to build a predictive model

1. Collect data relevant to your target of analysis.
2. Organize data into a single dataset.
3. Clean your data to avoid a misleading model.
4. Create new, useful variables to understand your records.
5. Choose a methodology/algorithm.
6. Build the model.

1. What is an example of a predictive model?
2. How much data do I need to build a predictive model?
3. Which algorithm is would you use to build a predictive model?

What is an example of a predictive model?

Examples include using neural networks to predict which winery a glass of wine originated from or bagged decision trees for predicting the credit rating of a borrower. Predictive modeling is often performed using curve and surface fitting, time series regression, or machine learning approaches.

How much data do I need to build a predictive model?

There is no rule about how much data you need for your predictive modeling problem.

Which algorithm is would you use to build a predictive model?

Predictive modeling algorithms include logistic regression, time series analysis and decision trees.