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The project includes:\n- A **Flask-based web application** for interactive text classification.\n- **Preprocessing** of text data, including cleaning, tokenization, and lemmatization.\n- Training and evaluation of multiple models, including:\n  - Traditional ML models: Logistic Regression, SVM, Naive Bayes, Random Forest, Gradient Boosting, AdaBoost, and an Ensemble model.\n  - Deep learning models: LSTM, GRU, CNN, and a hybrid LSTM+CNN model.\n  - Fine-tuning of transformer-based models: BERT and XLNet using **ktrain**.\n- Visualization of results, including confusion matrices, accuracy plots, and word clouds.\n\n---\n\n# Requirements:\n\n* Python \n\n* Scikit-learn\n\n* TensorFlow \n\n* Keras\n\n# Dataset:\n\nThe dataset used in this project is the bbc-tex dataset, which consists of approximately 2225 text.\n\n# Results:\nThe results of each model on the bbc-text dataset are as follows:\n\n|  Model | Accuracy |\n|----------|----------|\n| Logistic Regression | 96.58% |\n| Support Vector Machine | 96.94% |\n| Multinomial Naive Bayes | 94.97% |\n| Randomforest | 95.15% |\n| GradientBoostingClassifier | 94.25% |\n| Ensemble Classifier | 97.12% |\n| AdaBoost | 94.43% |\n| LSTM 1-Layer | 99.22% |\n| LSTM 2-Layers | 97.78% |\n| GRU | 91.74% |\n| CNN+LSTM | 98.73% |\n| BERT | 99.60% |\n| XLNet | 99.46% |\n\n\n\n# Application Interface\n\n\u003cimg src=\"ui/ui.PNG\" alt=\"Original Image\" width=\"700\"\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsnigdho8869%2Fmulticlass-text-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsnigdho8869%2Fmulticlass-text-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsnigdho8869%2Fmulticlass-text-classification/lists"}