https://github.com/quickheaven/scs-3251-statistics-for-data-science

Repository dedicated to Term Project of UofT Statistics for Data Science Course
https://github.com/quickheaven/scs-3251-statistics-for-data-science

data-science jupyter-notebook logistic-regression numpy pandas python

Last synced: about 16 hours ago
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Repository dedicated to Term Project of UofT Statistics for Data Science Course

• Host: GitHub
• URL: https://github.com/quickheaven/scs-3251-statistics-for-data-science
• Owner: quickheaven
• License: mit
• Created: 2023-06-27T23:59:22.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-09-02T00:25:14.000Z (about 1 year ago)
• Last Synced: 2023-09-03T01:13:24.027Z (about 1 year ago)
• Topics: data-science, jupyter-notebook, logistic-regression, numpy, pandas, python
• Language: Jupyter Notebook
• Homepage:
• Size: 6.46 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Spam Email Classification

SCS 3251 Statistics for Data Science Project

Jupyter Notebooks:

* [Project Spambase](https://nbviewer.org/github/quickheaven/scs-3251-statistics-for-data-science/blob/5721d97fecd5b56f98a38e33a36ee0b459aff6af/project_spambase_nb.ipynb)

* [Spambase Dataset Loader](https://nbviewer.org/github/quickheaven/scs-3251-statistics-for-data-science/blob/5721d97fecd5b56f98a38e33a36ee0b459aff6af/spambase_dataset_loader_nb.ipynb)

* [Spambase Model Helper](https://nbviewer.org/github/quickheaven/scs-3251-statistics-for-data-science/blob/5721d97fecd5b56f98a38e33a36ee0b459aff6af/spambase_model_helper_nb.ipynb)

Team members:

| Name | Github Repo |

| --- | --- |

| Arjie Cristobal  | https://github.com/quickheaven |

## Introduction

### Spambase Dataset

The "spam" concept is diverse: advertisements for products/web sites, make money fast schemes, chain letters, pornography...

	

Our collection of spam e-mails came from our postmaster and individuals who had filed spam.  Our collection of non-spam e-mails came from filed work and personal e-mails, and hence the word 'george' and the area code '650' are indicators of non-spam.  These are useful when constructing a personalized spam filter.  One would either have to blind such non-spam indicators or get a very wide collection of non-spam to generate a general purpose spam filter.

### Dataset Source:

* Hopkins,Mark, Reeber,Erik, Forman,George, and Suermondt,Jaap. (1999). Spambase. UCI Machine Learning Repository. https://doi.org/10.24432/C53G6X.

Link: [Spambase](https://archive.ics.uci.edu/dataset/94/spambase)

## Presentation