A Natural Language Processing (NLP) project that automatically identifies the language of a given text using machine learning techniques. The project applies TF-IDF feature extraction and compares multiple classification algorithms to accurately detect text across multiple languages.
Language detection is one of the fundamental tasks in Natural Language Processing. It is widely used in translation systems, multilingual search engines, chatbots, social media platforms, and content recommendation systems.
This project builds an end-to-end language detection pipeline using machine learning, from data preprocessing and feature engineering to model evaluation and prediction.
- Text preprocessing and cleaning
- Exploratory Data Analysis (EDA)
- TF-IDF feature extraction
- Label Encoding
- Naive Bayes Classifier
- Logistic Regression Classifier
- Model comparison
- Confusion Matrix visualization
- Classification Report
- Cross-validation
- Language prediction for custom text
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
- NLTK
- WordCloud
- Jupyter Notebook
Language Detection Dataset
The dataset contains multilingual text samples belonging to 17 different languages.
- Text
- Language
- English
- Hindi
- Tamil
- Kannada
- Malayalam
- Arabic
- German
- French
- Spanish
- Portuguese
- Italian
- Russian
- Swedish
- Dutch
- Turkish
- Danish
- Greek
- TF-IDF Vectorizer
- Multinomial Naive Bayes
- Complement Naive Bayes
- Logistic Regression
- Accuracy
- Precision
- Recall
- F1-Score
- Confusion Matrix
- Cross Validation Score
Dataset
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Data Cleaning
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Text Preprocessing
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Exploratory Data Analysis
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TF-IDF Vectorization
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Train-Test Split
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Model Training
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Model Evaluation
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Language Prediction
language-detection/
│── language_detection.ipynb
│── README.md
│── requirements.txt
│── dataset/
│── images/
└── outputs/
- Built an end-to-end multilingual text classification pipeline.
- Compared multiple machine learning algorithms for language detection.
- Used TF-IDF vectorization for feature extraction.
- Evaluated models using standard classification metrics.
- Visualized model performance with confusion matrices and data visualizations.
git clone https://github.com/YOUR_USERNAME/language-detection.gitcd language-detectionpip install -r requirements.txtjupyter notebookOpen:
language_detection.ipynb
- Deep Learning (LSTM/BiLSTM)
- Transformer Models (BERT/XLM-RoBERTa)
- FastAPI Deployment
- Streamlit Web App
- Real-time Language Detection API
- Support for Additional Languages
This project is intended for educational and research purposes.
Rithvik Jaswal
Computer Science Engineering Student
Interested in Machine Learning, Natural Language Processing, Artificial Intelligence, and Data Science.
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