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An n-gram is a contiguous sequence of n items from a given sample of text or speech. By default, `TfidfVectorizer` uses a unigram approach, but specifying `ngram_range=(1,2)` means that both unigrams and bigrams will be considered.\n\nThe `analyzer` parameter in `TfidfVectorizer` specifies the type of analysis to be performed. By setting `analyzer='char'`, the vectorizer will generate character-level n-grams instead of word-level n-grams.\n\nUsing `TfidfVectorizer` from the `feature_extraction.text` module in the `scikit-learn` library, we can generate numerical representations of text data based on term frequency and inverse document frequency. By specifying `ngram_range=(1,2)` and `analyzer='char'`, we can consider both unigrams and bigrams at the character level.\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvishal815%2Flanguage_predictor_ml_nlp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvishal815%2Flanguage_predictor_ml_nlp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvishal815%2Flanguage_predictor_ml_nlp/lists"}