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In a diverse country like India, socio-economic factors like religion, caste, language, income along with other common physical, professional based factors, play a vital role while searching for spouse. With surge of Internet connectivity, online matrimonial websites have become hugely popular to cater such needs. Most of the users registered on these portals have genuine intention of finding their desired life partner, however due to various factors it attracts few people with no genuine intention for the same. Such users are known as Fake/Spam profiles. These people lead to bad user experience as well as revenue loss for the online matrimony business. In this thesis we present an approach to identify such users suing machine learning techniques. Due to lack of large labelled examples for fake / suspicious users, we solve the above problem as anomaly detection problem. In this thesis, we use autoencoder which is widely used for anomaly detection. We capture user’s behaviour, profile information and edit history to detect him/her as in-genuine or genuine profile. We then treat this problem as a reconstruction task using autoencoder which is trained on a set of genuine profiles features. While prediction, the autoencoder shows small reconstruction error for genuine profiles and a very high reconstruction error for the fake users and detect them. The proposed system produces 91.76% accuracy with 90.2% recall for fake class. To the best of our knowledge, this is the first study done to detect fake/spam user profiles in online matrimony domain.