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At Present Social Network Sites Are Part Of The Life For Most Of The People. Every Day Several People Are Creating Their Profiles On The Social Network Platforms And They Are Interacting With Others Independent Of The User’s Location And Time. The Social Network Sites Not Only Providing Advantages To The Users And Also Provide Security Issues To The Users As Well Their Information. To Analyze, Who Are Encouraging Threats In Social Network We Need To Classify The Social Networks Profiles Of The Users. From The Classification, We Can Get The Genuine Profiles And Fake Profiles On The Social Networks. Traditionally, We Have Different Classification Methods For Detecting The Fake Profiles On The Social Networks. But, We Need To Improve The Accuracy Rate Of The Fake Profile Detection In The Social Networks. In This Paper We Are Proposing Machine Learning And Natural Language Processing (NLP) Techniques To Improve The Accuracy Rate Of The Fake Profiles Detection. We Can Use The Support Vector Machine (SVM) And Naïve Bayes Algorithm.

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