📝 Project Overview This project focuses on building an efficient language detection system using machine learning. It predicts the language of any given sentence based on patterns learned from a labeled multilingual dataset.
Language identification is a critical task in many global applications including:
Translation engines
Chatbots
Social media moderation
Customer support routing
The dataset contains over 20 different languages and the model is trained to classify text samples into their respective languages using supervised learning.
📂 Dataset Description Source: Kaggle (or other open-source corpus)
Column Name Description Text The sentence or phrase to classify Language The actual language label
Languages Included: English, French, Spanish, German, Portuguese, Italian, Dutch, Turkish, and more.
🛠 Tools & Libraries Used Python
Pandas and NumPy – data manipulation
Scikit-learn – ML models and pipeline
TfidfVectorizer – feature extraction
Multinomial Naive Bayes – classification model
Matplotlib / Seaborn – visualization
🔁 Project Workflow text Copy Edit
- Import Required Libraries
- Load and Explore the Dataset
- Data Preprocessing
- Feature Extraction using TF-IDF
- Train-Test Split
- Train Classification Model (Naive Bayes)
- Evaluate Model Performance
- Predict Language for New Text ✅ Key Results Achieved high accuracy (over 95%) on validation set.
TF-IDF helped capture important n-gram patterns for different languages.
Successfully detected 20+ languages with simple classical ML models.
📌 Folder Structure (Recommended) cpp Copy Edit language-detection-ml/ │ ├── data/ │ └── language_detection.csv │ ├── notebook/ │ └── language_detection.ipynb │ ├── models/ │ └── nb_language_model.pkl (optional) │ ├── README.md └── requirements.txt 🚀 How to Run Clone the repository: Open language_detection.ipynb in Jupyter
Follow the steps for training and testing
📈 Future Improvements Use deep learning models (LSTM / BERT) for better contextual accuracy.
Add language confidence scores.
Deploy the model using Flask/Streamlit for real-time language detection.
🙌 Acknowledgements Kaggle and open-source contributors for the dataset.
Scikit-learn for classical ML pipelines.