<commit_before>---
layout: docs
title: Classification Quick Start
---

# Quick Start - Classification Engine Template

An engine template is a basic skeleton of an engine. PredictionIO's Classification Engine Template (/templates/scala-parallel-classification) has integrated **Apache Spark MLlib**'s Naive Bayes algorithm by default.

The default use case of Classification Engine Template is to predict the service plan (*plan*) a user will subscribe to based on his 3 properties: *attr0*, *attr1* and *attr2*.

You can customize it easily to fit your specific use case and needs.

We are going to show you how to create your own classification engine for production use based on this template.

## Install PredictionIO

First you need to [install PredictionIO {{site.pio_version}}]({{site.baseurl}}/install/)

**0.8.2 contains schema changes from the previous versions, if you have installed the previous versions, you may need to clear both HBase and ElasticSearch. See more [here](../resources/schema-change.html).**


Let's say you have installed PredictionIO at */home/yourname/predictionio/*.
For convenience, add PredictionIO's binary command path to your PATH, i.e. /home/yourname/predictionio/bin:

```
$ PATH=$PATH:/home/yourname/predictionio/bin; export PATH
```

and please make sure that PredictionIO EventServer, which collects data, is running:

```
$ pio eventserver
```



## Create a Sample App

Let's create a sample app called "MyApp2" now. An app prepresents the application that generates the data, e.g. a movie rating app.

```
$ pio app new MyApp2
```<commit_msg>Doc: minor spelling fix<commit_after>---
layout: docs
title: Classification Quick Start
---

# Quick Start - Classification Engine Template

An engine template is a basic skeleton of an engine. PredictionIO's Classification Engine Template (/templates/scala-parallel-classification) has integrated **Apache Spark MLlib**'s Naive Bayes algorithm by default.

The default use case of Classification Engine Template is to predict the service plan (*plan*) a user will subscribe to based on his 3 properties: *attr0*, *attr1* and *attr2*.

You can customize it easily to fit your specific use case and needs.

We are going to show you how to create your own classification engine for production use based on this template.

## Install PredictionIO

First you need to [install PredictionIO {{site.pio_version}}]({{site.baseurl}}/install/)

**0.8.2 contains schema changes from the previous versions, if you have installed the previous versions, you may need to clear both HBase and ElasticSearch. See more [here](../resources/schema-change.html).**


Let's say you have installed PredictionIO at */home/yourname/predictionio/*.
For convenience, add PredictionIO's binary command path to your PATH, i.e. /home/yourname/predictionio/bin:

```
$ PATH=$PATH:/home/yourname/predictionio/bin; export PATH
```

and please make sure that PredictionIO EventServer, which collects data, is running:

```
$ pio eventserver
```



## Create a Sample App

Let's create a sample app called "MyApp2" now. An app represents the application that generates the data, e.g. a movie rating app.

```
$ pio app new MyApp2
```
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{'dir': 'git-commits-cleaned', 'id': '1872940', 'n_tokens_mistral': 897, 'n_tokens_neox': 831, 'n_words': 407}