Employed Natural Language Processing techniques such as removal of Stopwords, Punctuations and Hyperlinks to prepare the Dataset(consisting 404290 rows) and also applied techniques such as Tokenization and Stemming • Extracted Basic features and Advance Features consisting of Fuzz features and explored the features importance • Transformed the texts to numerical vectors using TF-IDF Vectorizer and fitted Logistic Regression and Xgboost Model and did Hyperparameter Tuning on Xgboost model to get Auc score of 0.91 and accuracy 83%
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Employed Natural Language Processing techniques such as removal of Stopwords, Punctuations and Hyperlinks to prepare the Dataset(consisting 404290 rows) and also applied techniques such as Tokenization and Stemming
sonalgaud12/Quora_QuestionPair
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Employed Natural Language Processing techniques such as removal of Stopwords, Punctuations and Hyperlinks to prepare the Dataset(consisting 404290 rows) and also applied techniques such as Tokenization and Stemming
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