| # π° Fake or Real News Classifier Using FastText |
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| [](https://www.python.org/) |
| [](https://fasttext.cc/) |
| [](https://huggingface.co/vikasgautam2003/Text_Classification_Using_FastText) |
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| > β‘ A **text classification project** that classifies news as **Fake or Real** using **FastText**. |
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| ## π Features |
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Train and test a FastText classifier |
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Support for CSV datasets |
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Preprocessing and simple text tokenization |
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Save/load trained FastText model |
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Ready for deployment or further experimentation |
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| ## π οΈ Tech Stack |
| - **Model** β FastText |
| - **Language** β Python 3.10+ |
| - **Libraries** β pandas, fasttext |
| - **Dataset** β Custom CSV dataset (`ecommerce_dataset.csv`) |
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| ## π Project Structure |
| ```bash |
| βββ .ipynb_checkpoints/ # Jupyter notebook checkpoints |
| βββ TextClassification.ipynb # Main notebook |
| βββ TextClassificationPart2.ipynb # Extended analysis notebook |
| βββ requirements.txt # Python dependencies |
| βββ .gitattributes # Git LFS configuration |
| βββ ecommerce.train # Training dataset (LFS) |
| βββ ecommerce.test # Test dataset (LFS) |
| βββ ecommerce_dataset.csv # Full dataset (LFS) |
| βββ README.md # Project documentation |
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| βοΈ Setup Instructions |
| 1οΈβ£ Install Dependencies |
| pip install -r requirements.txt |
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| 2οΈβ£ Run Training / Evaluation |
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| Open the Jupyter notebooks: |
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| jupyter notebook TextClassification.ipynb |
| jupyter notebook TextClassificationPart2.ipynb |
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| Make sure to have datasets available locally (ecommerce.train, ecommerce.test, ecommerce_dataset.csv) or tracked via Git LFS. |
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| π― Use Cases |
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| Detect fake news in real-time applications |
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| Build NLP portfolio projects |
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| Experiment with FastText embeddings and text classification |
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| π‘ Future Improvements |
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| Add real-time API for classification |
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| Integrate with Hugging Face Datasets |
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| Hyperparameter tuning for better accuracy |
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| Add visualization dashboards for analysis |
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| π¨βπ» Author |
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| Vikas Gautam |