Fake review detection

[viththagi]

A machine learning model which can predict whether an online review is fraudulent or not. The main idea used to detect the fake nature of reviews is that the review should be computer generated through unfair means. If the review is created manually, then it is considered legal and original.

Detection of fake reviews out of a massive collection of reviews having various distinct categories with each review having a corresponding rating, label i.e. CG(Computer Generated Review) and OR(Original Review generated by humans) and the review text.

Main task is to detect whether a given review is fraudulent or not. If it is computer generated, it is considered fake otherwise not.

Techniques used: Removing punctuation character Transforming text to lower case Eliminating stopwords Stemming Lemmatizing Removing digits Transformers Used for Text Vectorization, Weighting and Normalization: CountVectorizer Bag of Words Transformer TFIDF(Term Frequency-Inverse Document Frequency) Transformer

I would like to work on these issue

[github-actions[bot]]

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[sreesti07]

Could you assign this to me? I'm interested. I have hands-on Python and Machine Learning.

[Satyam0775]

I requested to assign me the issue. @sanjay-kv

[sanjay-kv]

Let me know @sreesti07 if you want to collaborate with @Satyam0775

[Madhusri02]

@Satyam0775 I will collabrate with @sreesti07 ,

[sreesti07]

Yea I am okay to collaborate with @Satyam0775

[Chandrikajoshi123]

I am excited to tackle this issue. I requested you to assign me

[Satyam0775]

Yea I am okay to collaborate with @Satyam0775

okey

[HarshSaini23]

Hello Sir i am also want to contribute in this issue .

[github-actions[bot]]

This issue has been automatically closed because it has been inactive for more than 30 days. If you believe this is still relevant, feel free to reopen it or create a new one. Thank you!