maillouxc / datamining-project

This is a project for FGCU's knowledge discovery and data mining course.

datamining-project

This is a project for FGCU's knowledge discovery and data mining course. It focuses on spam email classification.

Part 1 of the project is an implementation of a spam email classifier from scratch in Java 8, using a Naive Bayes classifier, and also a K-nearest-neighbors classifier.

Part 2 of the project is a pair of experiments in Scikit learn with various other algorithms.

Run instructions:

Ensure that the dataset meets the following criteria:

• All files must be .txt files
• All files must be stored alone in their directory with no other files
• All files be divided between two subdirectories, test and training
• All spam emails are expected to have a filename beginning with "sp"

Usage:

Part 1:

usage: Classify 
  data_path
  algorithm_name (Either "knn" or "naivebayes")
  [k] The K value to use - only used if algorithm is "knn"

Part 2:

usage: python experiments.py

There are no arguments - the program is merely a script which conducts experiments with various values.

Run instuctions:

Part 1:

1. Ensure that you have Java JRE 8 installed.
2. Compile the program using Gradle: (From the project's part1 subdirectory) ./gradlew build The generated Java class files will then be in the /build/classes/java/main directory. (The program can also be compiled like a standard java program if this has problems).
3. Run the program as java Classify arguments The arugments are described in the above section

Part 2:

1. Ensure that you have Python 3.7 or greater installed. If you have both Python 2 and Python 3 installed, you will need to ensure you run the program using python3.
2. Ensure that you have pip installed. If you have both Python 2 and Python 3 installed, you will need to run pip commands using pip3.
3. Install scikit-learn: pip install sklearn
4. Install numpy: pip install numpy
5. Run the program as: python experiments.py path_to_data