Language Detection Model Machine Learning & NLP

The main purpose of this project is to build a machine learning model to detect a given text’s language by using Natural Language Processing (NLP). The objective is to make a model that can automatically understand the language of a text, which is useful for applications such as bilingual chatbots and content categorization.

Steps:

Data Collection and Preprocessing: Gether a datset with txt il plz show examples in many languages.Every text must be preprocessed (for example, it is necessary to tokenize and remove stop-words) in order to enhance model performance.

Gather data set of text samples in different languages.

Preprocess the text (e.g., subness and removing the stop word) to be a high-performance model.

Feature Extraction: The NLP models that are used in TF-IDF or word embeddings are the one that take the information from the text.

Come through the use of NLP techniques such as TF-IDF or word embeddings in order to grab data points from the text.

Model Building: Let's train the machine learning model too (for example, Logistic Regression, SVM, or Naive Bayes) for the classification of languages.

Spotting-Create a simple interface to input text and determine the recognized language.

Estimation of the Model:Check the result of our model via various metrics like Accuracy, Precision, Recall& F1 Score.

Regression Analysis:Observe the model’s performance with the help of accuracy, precision, and F1 scores as well as recall.

Deployment: Enable a user to integrate a simple user interface to input text and obtain the language detected.

Foresee a simple user interface to enter a text and then get the detected language to the user.

Programming Language: Python

Libraries: Scikit-learn, NLTK, Pandas

Tools: Jupyter Notebooks, Gradio, and Flask for deployment.