https://github.com/harshal306/message-spam-detection

Project to explore the results of machine learning techniques in SMS spam detection.
https://github.com/harshal306/message-spam-detection

Last synced: about 17 hours ago
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Project to explore the results of machine learning techniques in SMS spam detection.

• Host: GitHub
• URL: https://github.com/harshal306/message-spam-detection
• Owner: harshal306
• Created: 2017-05-19T21:14:33.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2017-04-15T18:29:31.000Z (over 7 years ago)
• Last Synced: 2025-01-02T19:53:17.522Z (10 days ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 209 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        

## University of Petroleum and Energy Studies Artificial Intelligence Group

[![Build Status](https://travis-ci.org/boennemann/badges.svg?branch=master)](https://travis-ci.org/boennemann/badges) ![Python](https://img.shields.io/badge/python-2.x-orange.svg)

![MIT](https://img.shields.io/github/license/mashape/apistatus.svg)

![Type](https://img.shields.io/badge/Machine-Learning-red.svg) ![Type](https://img.shields.io/badge/Type-Spervised-yellow.svg)

### Install

This project requires **Python 2.7** and the following Python libraries installed:

- [NumPy](http://www.numpy.org/)

- [Pandas](http://pandas.pydata.org)

- [Scikit-Learn](http://scikit-learn.org/stable/)

- [Matplotlib](http://matplotlib.org/users/installing.html/)

- [Seaborn](https://seaborn.pydata.org/installing.html/)

You will also need to have software installed to run and execute a [Jupyter Notebook](http://ipython.org/notebook.html)

If you do not have Python installed yet, it is highly recommended that you install the [Anaconda](http://continuum.io/downloads) distribution of Python, which already has the above packages and more included. Make sure that you select the Python 2.7 installer and not the Python 3.x installer.

### Code

Template code is provided in the `*.ipynb` jupyter notebook file. You will also be required to use the `data.csv` dataset file to complete your work. While some code has already been implemented to get you started, you will need to implement additional functionality when requested to successfully complete the project.

### Run

In a terminal or command window, navigate to the top-level project directory `student_intervention/` (that contains this README) and run one of the following commands:

```bash

ipython *.ipynb

```  

or

```bash

jupyter notebook *.ipynb

```

This will open the Jupyter Notebook software and project file in your browser.

### Data

The dataset used in this project is included as `data.csv`. This dataset is a freely available on the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/). This dataset has the following attributes:

**Features**

1.  `Features`: Description

**Target Variable**

4. `Target`: Description

###