Abstract:
In this modern age, social media is a part and parcel of our everyday life. Our daily life is
undergoing multiple changes that directly have positive and negative effects. Social media has
a positive and negative side too. Sometimes it is helpful but sometimes harmful. Facebook is one of
the social media which uses mostly comparing to others. There are some changes in our daily life
by using it. We are not concern about our timetable and we are scrolling Facebook without any
need. The main reason behind Facebook is communication. Now it is not only communication but
also wasting some valuable time. It is called addiction and most people eat, sleep and repeat
Facebook. It is quite hard to predict Facebook addiction without collecting and testing data.
Prediction is much easier when there is “Machine Learning”. The main purpose of the current
study is to predict the addiction of people on Facebook and awareness of their daily routine with high
accuracy. For the prediction of Facebook addiction, we use seven ‘Machine Learning’
classification algorithms such as Support Vector Machine (SVM), Decision Tree (DT), Naïve
Bayes (NB), Linear Regression (LR), Logistic Regression (LR), Linear Discriminant Analysis
(LDA), k-Nearest Neighbors (kNN). After using algorithms, we use Principal Component Analysis
(PCA) for reducing data mathematically. Our finding demonstrates that kNN with a greater accuracy
rate (93.53%) outperforms the k-Nearest Neighbors.