Exploratory Data Analysis and Feature Selection for Social Media Hackers Prediction Problem

In machine learning, the intelligence of a developed model is greatly influenced by the dataset used for the target domain on which the developed model will be deployed. Social media platform has experienced more of hackers’ attacks on the platform in recent time. To identify a hacker on the platform, there are two possible ways. The first is to use the activities of the user while the second is to use the supplied details the user registered the account with. To adequately identify a social media user as hacker proactively, there are relevant user details called features that can be used to determine whether a social media user is a hacker or not. In this paper, an exploratory data analysis was carried out to determine the best features that can be used by a predictive model to proactively identify hackers on the social media platform. A web crawler was developed to mine the user dataset on which exploratory data analysis was carried out to select the best features for the dataset which could be used to correctly identify a hacker on a social media platform.