Machine Learning- Sentiment Analysis : Email Spam Detection.

https://github.com/rags2231/Email_spam_classifier

For an email spam classifier, the initial phase involves data preprocessing to prepare the dataset for analysis. This includes tasks such as text normalization, where words are converted to lowercase and punctuation is removed, as well as tokenization, where the text is split into individual words or tokens. Stop words—common words like "the" or "and"—are often removed to reduce noise. Following this, the dataset is split into training and testing sets to evaluate the classifier's performance accurately.

The next step is feature extraction, where relevant features from the email content are identified. This can include word frequencies, presence of specific keywords, or even more advanced features like TF-IDF (Term Frequency-Inverse Document Frequency). These features serve as input to the classifier algorithm.

Various machine learning algorithms can be employed for classification, such as Naive Bayes, Support Vector Machines (SVM), or even deep learning techniques like Recurrent Neural Networks (RNNs) or Convolutional Neural Networks (CNNs) for more complex models.

Once the classifier is trained, its performance is evaluated using the testing set, typically using metrics like accuracy, precision, recall, and F1-score. Iterative refinement may be necessary to improve performance, including feature selection, parameter tuning, or trying different algorithms.

Finally, the trained classifier can be deployed into a production environment, where it can automatically classify incoming emails as spam or non-spam, helping users manage their email inboxes more efficiently and effectively.