During December 2024, Nithin Nallamothu developed an end-to-end hate speech detection capability for the springboardmentor891v/HATE_SPEECH_DETECTION_INFOSYS_INTERNSHIP_OCT2024 repository. He designed and implemented a machine learning pipeline using Python and Scikit-learn, leveraging Pandas for data handling and NLTK for natural language processing. The workflow included data loading, TF-IDF preprocessing, model training with a Random Forest classifier, and thorough evaluation. Nithin also provided a live demonstration of predictions on sample data, ensuring reproducibility through a Colab-based prototype. This work established a practical baseline for automated content moderation, offering a foundation for integration into real-world moderation workflows.
December 2024 performance summary for the HATE_SPEECH_DETECTION_INFOSYS_INTERNSHIP_OCT2024 project. Delivered an end-to-end Hate Speech Detection capability using a Random Forest classifier, including data loading, TF-IDF preprocessing, model training, evaluation, and a live demonstration of predictions on sample data. All work is tracked in the repository springboardmentor891v/HATE_SPEECH_DETECTION_INFOSYS_INTERNSHIP_OCT2024, with Colab-based prototypes documented in two commits. This serves as a reproducible baseline for automated content moderation and can be integrated into moderation workflows.
December 2024 performance summary for the HATE_SPEECH_DETECTION_INFOSYS_INTERNSHIP_OCT2024 project. Delivered an end-to-end Hate Speech Detection capability using a Random Forest classifier, including data loading, TF-IDF preprocessing, model training, evaluation, and a live demonstration of predictions on sample data. All work is tracked in the repository springboardmentor891v/HATE_SPEECH_DETECTION_INFOSYS_INTERNSHIP_OCT2024, with Colab-based prototypes documented in two commits. This serves as a reproducible baseline for automated content moderation and can be integrated into moderation workflows.
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