kambasht / spam-email-classification

Classifying Spam emails with the help of countvectorizer, tf-idf and linear svc

Spam-Email-Classification

Classifying Spam emails with the help of countvectorizer, tf-idf and linear svc

Making use of

1. tokenization through WordNetLemmatizer

2. tf-idf transformer[https://kavita-ganesan.com/tfidftransformer-tfidfvectorizer-usage-differences/#.Xoesx4hKiUk]

3. Linear SVC for classification [https://www.youtube.com/watch?v=efR1C6CvhmE]

4. Gridsearch Cross validation to find optimal parameters for classifier model[https://www.youtube.com/watch?v=fSytzGwwBVw]

5. Evaluate the model using plot_precision_recall_curve to ensure we are covering our bases in case of an imbalanced datasets [https://machinelearningmastery.com/roc-curves-and-precision-recall-curves-for-imbalanced-classification/]

License and data : https://www.kaggle.com/karthickveerakumar/spam-filter