A spam classifier for classifying mails based on TF-IDF on Keras.
This GitHub repository contains a spam classifier implemented in Python using NLTK, TF-IDF representations, and TensorFlow. The spam classifier is a machine learning model designed to identify and classify email or text messages as either spam or legitimate (ham).
-
Data Preprocessing: The repository provides a comprehensive set of data preprocessing techniques using the Natural Language Toolkit (NLTK). This includes tokenization, stop word removal, stemming, and other text normalization techniques that help to transform raw text data into a format suitable for machine learning models.
-
TF-IDF Representation: The spam classifier uses the Term Frequency-Inverse Document Frequency (TF-IDF) representation to vectorize text data. This technique assigns weights to each term based on its frequency in a document and its rarity across the entire corpus, allowing the model to capture the importance of each term in distinguishing spam from legitimate messages.
-
TensorFlow Implementation: The spam classifier is built using TensorFlow, a popular deep learning framework. The repository provides an implementation of a neural network model that utilizes dense layers for classification. The model is trained on preprocessed text data and optimized using gradient descent algorithms.
-
Model Evaluation: The repository includes evaluation metrics and techniques to assess the performance of the spam classifier. This includes accuracy, precision, recall, F1 score, and confusion matrix analysis, enabling users to measure the effectiveness of the classifier and make informed decisions about its usage.
-
Usage Examples: The repository contains example scripts and notebooks demonstrating how to train and use the spam classifier. These examples provide step-by-step instructions, sample datasets, and code snippets to help users understand the implementation and apply it to their own spam classification tasks.
To get started with the spam classifier, clone the repository to your local machine using the following command:
git clone [https://github.com/Amanp16/Spam_Classifier]
Once you have cloned the repository, you can explore the code and example notebooks to understand the implementation and use the spam classifier for your own datasets. The repository also provides instructions for installing the necessary dependencies and running the code.