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Auto ML Single and Multi Label Text Classification in Python Projects
र 1000
Auto ML Single and Multi Label Text Classification in Python Projects
Technical Details
Domain : Python
Database : Sqlite
Tools : Anaconda
Run Tools: VS Code
Database : Sqlite
Tools : Anaconda
Run Tools: VS Code
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About This Product
Auto ML Single and Multi Label Text Classification in Python Projects
Abstract
Text classification is a fundamental task in natural language processing (NLP) with applications such as spam filtering, sentiment analysis, news categorization, and medical report tagging. Traditionally, text classification models are manually designed and tuned, which requires domain expertise and considerable time. This project, AutoML Single and Multi-Label Text Classification in Python, aims to automate the entire pipeline of text preprocessing, feature extraction, model selection, and hyperparameter tuning. By leveraging AutoML frameworks such as AutoKeras, TPOT, and H2O AutoML, the system can handle both single-label classification (where each text belongs to only one category) and multi-label classification (where a text can belong to multiple categories simultaneously). Implemented in Python using libraries such as Scikit-learn, TensorFlow/Keras, Pandas, and NLTK/Spacy, the project enables high-accuracy, efficient, and scalable text classification without extensive manual intervention.
Existing System
Existing text classification approaches are often built using manually designed pipelines. Preprocessing involves tokenization, stopword removal, and TF-IDF/word embedding creation, followed by the selection of traditional ML models like SVM, Naïve Bayes, or Logistic Regression, or deep learning models like CNNs, RNNs, and Transformers. These models require manual feature engineering and hyperparameter tuning, which is both time-consuming and dependent on expert knowledge. Additionally, most existing systems focus primarily on single-label classification and struggle to adapt to multi-label classification tasks, where texts may belong to multiple overlapping categories.
Proposed System
The proposed system introduces an AutoML-based framework that automates the end-to-end pipeline for both single and multi-label text classification. Raw text data is preprocessed using NLP techniques (tokenization, lemmatization, embeddings), and the AutoML engine automatically selects the best feature representation (TF-IDF, Word2Vec, BERT embeddings) along with the most suitable classification model. For multi-label tasks, problem transformation methods (Binary Relevance, Classifier Chains) or specialized deep learning architectures are used. The system continuously evaluates multiple models, optimizes hyperparameters, and outputs the best-performing classifier. Compared to existing systems, this approach offers automation, adaptability, high accuracy, and scalability while reducing the need for human expertise in model design.
Abstract
Text classification is a fundamental task in natural language processing (NLP) with applications such as spam filtering, sentiment analysis, news categorization, and medical report tagging. Traditionally, text classification models are manually designed and tuned, which requires domain expertise and considerable time. This project, AutoML Single and Multi-Label Text Classification in Python, aims to automate the entire pipeline of text preprocessing, feature extraction, model selection, and hyperparameter tuning. By leveraging AutoML frameworks such as AutoKeras, TPOT, and H2O AutoML, the system can handle both single-label classification (where each text belongs to only one category) and multi-label classification (where a text can belong to multiple categories simultaneously). Implemented in Python using libraries such as Scikit-learn, TensorFlow/Keras, Pandas, and NLTK/Spacy, the project enables high-accuracy, efficient, and scalable text classification without extensive manual intervention.
Existing System
Existing text classification approaches are often built using manually designed pipelines. Preprocessing involves tokenization, stopword removal, and TF-IDF/word embedding creation, followed by the selection of traditional ML models like SVM, Naïve Bayes, or Logistic Regression, or deep learning models like CNNs, RNNs, and Transformers. These models require manual feature engineering and hyperparameter tuning, which is both time-consuming and dependent on expert knowledge. Additionally, most existing systems focus primarily on single-label classification and struggle to adapt to multi-label classification tasks, where texts may belong to multiple overlapping categories.
Proposed System
The proposed system introduces an AutoML-based framework that automates the end-to-end pipeline for both single and multi-label text classification. Raw text data is preprocessed using NLP techniques (tokenization, lemmatization, embeddings), and the AutoML engine automatically selects the best feature representation (TF-IDF, Word2Vec, BERT embeddings) along with the most suitable classification model. For multi-label tasks, problem transformation methods (Binary Relevance, Classifier Chains) or specialized deep learning architectures are used. The system continuously evaluates multiple models, optimizes hyperparameters, and outputs the best-performing classifier. Compared to existing systems, this approach offers automation, adaptability, high accuracy, and scalability while reducing the need for human expertise in model design.
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