supervised-machine-learning-algorithms-for-spam-email-detection's Introduction

Supervised-Machine-Learning-Algorithms-for-Spam-Email-Detection

Supervised Machine Learning Algorithms used for ENRON spam Email dataset

The algorithms used for this implementation are:
1. Naive Bayes Algorithm (Python implementation)
2. Perceptron Algorithm (JAVA implementation)
3. C4.5 Algorithm (JAVA implementation)

The dataset used is ENRON SPAM EMAIL DATASET (available online for free)
Link- http://www2.aueb.gr/users/ion/data/enron-spam/
Note:We have used Enron1 only for implementation.

OUTPUT:

The Algorithms are applied on the spam email dataset and assessed i.e. It provides details on the implemented alogrithms on the basis of the given parameters:
1. Recall
2. FP - Rate
3. Precision
4. Accuracy
5. F - measure

Software Requirements:
1. JAVA library version - 8.0 (minimum)
2. Python library version - 2.7 (minimum)
3. nltk library for Python Available Here

Implementation:

1. Extract the dataset.zip file into all 3 folders as training and testing folders.
2. Run the code in respective compilers.

Further details on
1. Distribution of dataset (Distributed in 65:35 ratio of training:testing)
2. Details of output parameters
3. Usage and applications of Algorithms
can be found here

For any query or help feel free to raise an issue

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