During December 2024, Nagaraju Bellam worked on the HATE_SPEECH_DETECTION_INFOSYS_INTERNSHIP_OCT2024 repository, developing an end-to-end hate speech detection workflow. He built and evaluated multiple machine learning classifiers, including Logistic Regression and Random Forest, using Python and scikit-learn to establish a scalable moderation pipeline. Nagaraju expanded the dataset, curated large labeled data, and documented the process in Jupyter notebooks to support reproducibility and future collaboration. He also maintained repository hygiene by decommissioning the project, removing obsolete scripts and data. His work demonstrated depth in data preprocessing, model training, and project management, resulting in a reusable workflow for automated text moderation.
December 2024 monthly summary for HATE_SPEECH_DETECTION_INFOSYS_INTERNSHIP_OCT2024. Focused on delivering an end-to-end hate speech detection workflow, expanding data resources, and maintaining repository hygiene. Key activities included: (1) ML Model Development: built and evaluated baseline and multiple classifiers (Linear Regression, Logistic Regression, Decision Tree, SVM, Naive Bayes, Random Forest) to establish a scalable detection pipeline; (2) Dataset Expansion and Documentation: added training data, notebooks, and comprehensive project documentation, including a Jupyter notebook and large labeled datasets to support reproducibility; (3) Maintenance/Cleanup: decommissioned the project by removing the Hate Speech Detection directory and related scripts/data to prevent stale artifacts. Major bugs fixed/maintenance: cleanup and artifact removal to ensure repository integrity. Overall impact: established a reusable ML workflow for automated moderation, expanded data resources for future work, and improved repo hygiene for efficient collaboration. Technologies/skills demonstrated: Python, scikit-learn, ML model development and evaluation, data curation, Jupyter notebooks, version control, and documentation.
December 2024 monthly summary for HATE_SPEECH_DETECTION_INFOSYS_INTERNSHIP_OCT2024. Focused on delivering an end-to-end hate speech detection workflow, expanding data resources, and maintaining repository hygiene. Key activities included: (1) ML Model Development: built and evaluated baseline and multiple classifiers (Linear Regression, Logistic Regression, Decision Tree, SVM, Naive Bayes, Random Forest) to establish a scalable detection pipeline; (2) Dataset Expansion and Documentation: added training data, notebooks, and comprehensive project documentation, including a Jupyter notebook and large labeled datasets to support reproducibility; (3) Maintenance/Cleanup: decommissioned the project by removing the Hate Speech Detection directory and related scripts/data to prevent stale artifacts. Major bugs fixed/maintenance: cleanup and artifact removal to ensure repository integrity. Overall impact: established a reusable ML workflow for automated moderation, expanded data resources for future work, and improved repo hygiene for efficient collaboration. Technologies/skills demonstrated: Python, scikit-learn, ML model development and evaluation, data curation, Jupyter notebooks, version control, and documentation.
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