To be able to test the predictive analysis model you built, you need to split your dataset into two sets: training and test datasets. These datasets should be selected at random and should be a good representation of the actual population. Similar data should be used for both the training and test datasets.

## How can prediction model accuracy be tested?

Accuracy is defined as the percentage of correct predictions for the test data. It can be calculated easily **by dividing the number of correct predictions by the number of total predictions**.

## How do you do predictive modeling?

**The steps are:**

- Clean the data by removing outliers and treating missing data.
- Identify a parametric or nonparametric predictive modeling approach to use.
- Preprocess the data into a form suitable for the chosen modeling algorithm.
- Specify a subset of the data to be used for training the model.

## What is a good prediction accuracy?

If you are working on a classification problem, the best score **is 100% accuracy**. If you are working on a regression problem, the best score is 0.0 error. These scores are an impossible to achieve upper/lower bound. All predictive modeling problems have prediction error.

## What are the most processes in creating predictive models?

**Predictive modeling** is the **process** of using known results **to create**, **process**, and validate a **model** that can be used to **make** future predictions. Two of the **most** widely used **predictive modeling** techniques are regression and neural networks.