Natural-Language-Processing-Language-gusser

This is the code(syntax) used for the Language gusseing, made as a project on topic Machine Learning. With assistance from the Android Club of our University.

The project title has been identified as Natural Language Processing - Language Guesser.

# 🌍 Natural Language Processing - Language Guesser

This repository contains the code and resources for a community machine learning project focused on **Language Identification**. The goal of this project is to develop a machine learning model capable of guessing or identifying the language of a given text input.

This project was developed on the topic of Machine Learning with assistance and collaboration from the **Android Club of our University**.

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## ✨ Key Features

* **Language Identification Model:** A machine learning model, likely implemented using NLP techniques, to predict the language of text.
* **Python Implementation:** The core logic is built entirely in Python, leveraging common data science and ML libraries.
* **Training Dataset Included:** A dedicated training file (`final_train.csv`) is provided for reproducing or extending the model.

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## 🚀 Getting Started

Follow these instructions to set up the project locally and run the language guesser model.

### Prerequisites

You will need Python 3.x installed. It is highly recommended to use a virtual environment.

```bash
# Create a virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate  # On Linux/macOS
# .\venv\Scripts\activate # On Windows (PowerShell)

Installation

1. Clone the repository:

   git clone [https://github.com/INEcodes/Android-club-community-project.git](https://github.com/INEcodes/Android-club-community-project.git)
   cd Android-club-community-project

2. Install the necessary dependencies. Since this is a machine learning project, essential libraries like pandas, scikit-learn, and possibly numpy and nltk are required.

   A standard ML requirements file (e.g., requirements.txt) should be created for full dependency management. Assuming basic ML stack:

   pip install pandas scikit-learn numpy

Execution

The core logic of the model is contained in Final Code.py.

To run the model (e.g., to train it or make predictions):

python "Final Code.py"

You can open the Python file to see the implementation details, including data loading from final_train.csv and the machine learning algorithm used.

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📂 Repository Structure

The repository is straightforward and contains the essential files for the project:

Android-club-community-project/
├── Final Code.py           # The main Python script containing the ML model and logic.
├── final_train.csv         # The dataset used for training the language identification model.
└── README.md               # This README file.

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🤝 Community & Contribution

This project was built as a community effort, and further contributions are welcome!

1. Report Bugs: If you find any issues in the code, please report them on the Issues page.
2. Suggest Enhancements: Have an idea for a better model or new features? Open an issue to discuss it.
3. Contribute Code:
   • Fork the repository.
   • Create a feature branch (git checkout -b feature/NewAlgorithm).
   • Commit your changes (git commit -m 'feat: Add a better classification algorithm').
   • Push to the branch (git push origin feature/NewAlgorithm).
   • Open a Pull Request.