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ahmdrz/spam-classifier

By ahmdrz

•Updated over 7 years ago

Here are our exercises of implementing classification algorithms.

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ahmdrz/spam-classifier repository overview

⁠Classification algorithms (SpamClassifier)

Here are our exercises of implementing classification algorithms in Python using sci-kit learn.

It written in Python-3.6.7. Dependencies are available in requirements.txt file. You may have to install tkinter. Follow this instruction:

$ # on the debian-based OS like Ubuntu
$ sudo apt-get install python3-tk

Image is from developers.google.com⁠

You can see DOCUMENT.md for more information.

⁠Docker

To run this program without installing python3 and other libraries/dependencies, you can run our docker image.

$ docker pull ahmdrz/spam-classifier:latest
$ docker run ahmdrz/spam-classifier:latest
⁠Dataset

We used standard dataset named spambase. You can find it in dataset directory of our repository. This program support all of arff datasets that the class-label is in the last column.

⁠Algorithms
  1. kNN
  2. Naive bayes
  3. Decision tree
  4. SVM
  5. Random forest

TODO: With neural-networks

⁠Results

The result contains the confusion matrix and the accuracy of each algorithm and will be available in the results directory.

Accuracy graphConfusion matrix for kNN with k=6

The configuration of each classifier listed below

  1. n_neighbors in kNN: 6
  2. C in SVC: 2.0
  3. n_estimators in RandomForest: 6
  4. all others were in the default configuration.

We used confusion_matrix_pretty_print.py⁠ to generate this figure.

kNNSVMNaive-BayesRandom-ForestDecision-Tree
⁠Authors
  1. Nastaran Kiani (@Nastarankiani⁠)
  2. Ahmadreza Zibaei (@ahmdrz⁠)

Tag summary

Content type

Image

Digest

Size

480.3 MB

Last updated

over 7 years ago

docker pull ahmdrz/spam-classifier