Hate_Speech_Detection_NLP

This is Hate speech detection model created using Count Vectorizer and XGBoost Classifier with an Accuracy upto 0.9471, train-test split of 70:30, which can be used to predict whether tweets are hate or non-hate.

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Dataset:

• Dataset using Twitter data, isused to research hate-speech detection. The text is classified as: hate-speech, offensive language, and neither. Due to the nature of the study, it’s important to note that this dataset contains text that can be considered racist, sexist, homophobic, or generally offensive.

• Link for dataset: https://www.kaggle.com/mrmorj/hate-speech-and-offensive-language-dataset

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Tools used for project development:

• Python

• NLP

• Porter Stemmer

• Count Vectorizer

• XGBoost Classifier

• Random Forest Classifier

• Decision Tree

• Support Vector Machine

• Logistic Regression

• K Nearest Neighbours

• Gaussian Naive Bayes Classifier

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