rimmelasghar / Language-Detector-Model_Django

A Machine Learning Model that detects different language syntax.

LANGUAGE DETECTION MODEL DJANGO. 🉑🈂️🈷️🚀

🚩 Language-Detection 🤖🤖🚀

Language detection is a natural language processing task where we need to identify the language of a text or document. Using machine learning for language identification was a difficult task a few years ago because there was not a lot of data on languages, but with the availability of data with ease, several powerful machine learning models are already available for language identification. As a human, you can easily detect the languages you know. For example, I can easily identify Urdu and English, but being an Pakistani, it is also not possible for me to identify all languages. This is where the language identification task can be used. Google Translate is one of the most popular language translators in the world which is used by so many people around the world. It also includes a machine learning model to detect languages that you can use if you don’t know which language you want to translate.

Note 🕵️‍♂️:

The most important part of training a language detection model is data. The more data you have about every language, the more accurate your model will perform in real-time. The dataset that I am using is collected from Kaggle, which contains data about 22 popular languages and contains 1000 sentences in each of the languages, so it will be an appropriate dataset for training a language detection model with machine learning.

Important Note 🕵️‍♂️:

This model can only detect the languages mentioned in the dataset.

Prerequisites

• Pandas
• Numpy
• Sklearn
• Django

Multinomial Naive Bayes Algorithm 👨‍🔬

Multinomial Naive Bayes is one of the variations of the Naive Bayes algorithm in machine learning which is very useful to use on a dataset that is distributed multinomially. When there are multiple classes to classify, this algorithm can be used because to predict the label of the text it calculates the probability of each label for the input text and then generates the label with the highest probability as output.

Some of the advantages of using this algorithm for multinomial classification are:

It is easy to use on continuous and discrete data It can handle large data sets It can classify data with multiple labels Best to use for training natural language processing models

Installation 👨‍🔧👩‍🔧:

  pip install -r requirements.txt

open command prompt in your project folder and execute following command.

  python manage.py runserver

🚀 The project will run on http://localhost:8000/

Preview of the Application

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👨‍💻 By Rimmel Asghar with ❤