This project aims to build a sms spam Classifier using Natural Language Processing (NLP). All the necessary files are present in the repo. The dataset used is "sms spam collection dataset" from UCI machine learning . Natural Language Toolkit (NLTK) and scikit-learn are primarily used in this project
NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and an active discussion forum. For more details visit ( https://www.nltk.org/ )
Scikit-learn (Sklearn) is the most useful and robust library for machine learning in Python. It provides a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction via a consistence interface in Python. This library, which is largely written in Python, is built upon NumPy, SciPy and Matplotlib. For more details visit ( https://scikit-learn.org/stable/ ). Special Thanks to CampusX for its support. For more details visit ( https://medium.com/campusx ).
The pickle module in Python is used to serialize and deserialize objects. It converts Python objects into a byte stream that can be written to a file or transmitted over a network. The serialized data can later be loaded and reconstructed into Python objects. '.pkl' files are used to store various types of Python objects, including data structures, models, and more. They are especially useful when you want to save the state of an object or store it for later use.