https://github.com/saradindusengupta/email_spam_filter
This model mainly uses deep neural network to train and classify between spam and ham messages and compare them with SVM and random forest.
https://github.com/saradindusengupta/email_spam_filter
deep-learning keras-neural-networks python-3-5 sklearn tensorflow
Last synced: 2 months ago
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This model mainly uses deep neural network to train and classify between spam and ham messages and compare them with SVM and random forest.
- Host: GitHub
- URL: https://github.com/saradindusengupta/email_spam_filter
- Owner: saradindusengupta
- Created: 2017-10-20T07:21:26.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2017-10-22T08:35:07.000Z (over 8 years ago)
- Last Synced: 2025-03-12T14:46:31.634Z (over 1 year ago)
- Topics: deep-learning, keras-neural-networks, python-3-5, sklearn, tensorflow
- Language: Python
- Size: 205 KB
- Stars: 3
- Watchers: 1
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Email_Spam_Filter
This project uses deep neural network model to classify spam messages and compare the performance with other machine learning model such as Xgboost ,SVM and random forest. This project uses the Enron dataset available here : http://www2.aueb.gr/users/ion/data/enron-spam/.
This approach is combines unsupervised learning with Supervised learning. We will generate the features using TF-IDF algorithm and then use this to features to train Models on labeled enron data.
# Model trained and evaluated :
Deep Learning model trained using keras and tensorflow
SVM
Random Forest
XGboost
Deep learning model performs very well on this dataset
# Dependencies :
Language - Python 3.5
keras : https://keras.io/
tensorflow : https://www.tensorflow.org/
sklearn: http://scikit-learn.org/stable
numpy : http://www.numpy.org/
pickle: https://docs.python.org/2/library/pickle.html
seaborn: https://seaborn.pydata.org/
To run the model make sure the above dependencies are met and the dataset from [http://www2.aueb.gr/users/ion/data/enron-spam/] is at /home destination.
For linux environment use the chmod to grant execution permission
```
sudo chmod 777 spam-filter_classifier.py
```
Then type
```
python3 spam-filter_classifier.py // for Python 3 developemnt
```
The finding and analysis are available at spam-filter_classifier.pdf.