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Bret's assignment made $4500/hour working from home! You can too! #1

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a6a64ad
3 classifiers completed
BretRunestad Feb 10, 2015
569c78d
created function for process, ran things through extra tree classifier
BretRunestad Feb 10, 2015
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47 changes: 1 addition & 46 deletions README.md
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# Classifing spam

## Description

Use the Spambase dataset to classify spam. This data is already parsed down from email to features.

## Objectives

### Learning Objectives

After completing this assignment, you should understand:

* Simple Bayesian analysis
* The importance of separating training and test data

### Performance Objectives

After completing this assignment, you should be able to:

* Create a Bayesian classifier
* Train your classifier
* Test your classifier

## Details

### Deliverables

* A Git repo called spambase containing at least:
* `README.md` file explaining how to run your project
* a `requirements.txt` file

### Requirements

* No PEP8 or Pyflakes warnings or errors

## Normal Mode

Go to the UCI Machine Learning repository and [download the Spambase dataset](https://archive.ics.uci.edu/ml/datasets/Spambase). Make sure you [read the documentation for the data](https://archive.ics.uci.edu/ml/machine-learning-databases/spambase/spambase.DOCUMENTATION). This explains what the attributes are in the data file.

Subsample the data set so 60% is training data and 40% is test data. You can subsample however you like, including splitting the original file. Just make sure that you have a representative data set. (The original is about 60% not-spam and 40% spam.)

Then write code to classify the data into spam and not-spam, training with your training data and testing on your test data. Try multiple classifiers to see which gives you the highest success.

## Hard Mode

In addition to the normal mode requirements, try reducing or changing your features in order to get better results.
The entirety of my code and analysis is contained within an IPython Notebook folder titled spambase.ipynb.
1 change: 1 addition & 0 deletions requirements.txt
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ipython[all]
scikit-learn
pandas
numpy
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