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Feature Description
Implementation process- Data Preprocessing: Cleaning and preparing text data for analysis, including handling duplicates, converting text to lowercase, and removing special characters.
Exploratory Data Analysis: Understanding the distribution of toxic vs. non-toxic comments, visualizing word clouds, and exploring key metrics like precision, recall, and F1-score.
Model Development: Building a neural network model using TensorFlow/Keras, training it on the dataset with oversampling techniques like SMOTE, and evaluating its performance using classification metrics.
Evaluation: The model achieved a high accuracy of 96.38% on the test set, demonstrating its effectiveness in accurately identifying toxic comments.
Visualizations: Visualizations such as bar plots for toxic vs. non-toxic comments, model accuracy and loss plots, and a heatmap of classification report metrics enhance the understanding of the model's performance.
Use Case
Benefits
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Priority
High
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