The main aim of this project is to build an accurate and efficient machine learning model that can detect the language of an input text. Language detection plays a vital role in various applications like multilingual chatbots, social media filtering, and content translation services. Previously a challenging task due to limited data, language identification has now become achievable with large datasets and improved computational resources. This project leverages the N-gram model to create a unique "language fingerprint" for each language based on frequency distributions. By training a model on labeled datasets, the system can classify texts into languages with high precision. The outcome is expected to be a versatile and adaptable model that demonstrates a strong understanding of NLP and machine learning fundamentals, giving students real-world exposure to language processing challenges.
To successfully complete the project, students will follow a twelve-week plan. Initially, they will install Python and necessary machine learning libraries, along with development tools such as Anaconda Navigator or Google Colab. In the early stages, students will study the ML model architecture and gather raw, labeled text data in different languages.
They will then preprocess the text data, build an N-gram model, and train the machine learning algorithm to identify language patterns. The middle weeks will be dedicated to testing the model’s accuracy and refining it based on performance evaluations. Final stages include full-scale development, documentation, testing, and a team-based project presentation. Throughout the process, students must ensure uniform coding ethics, follow proper documentation guidelines, and avoid plagiarism. A laptop or PC with appropriate software and access to datasets is required to execute the project efficiently.
