Churn prediction is a use case common in machine learning. For all of you who don’t know, churn is the business word for leaving. Telco businesses also should understand why and how customer attrition happens. With an accurate and reliable churn prediction model, companies can implement processes and actions to prevent customers from leaving. Churn Prediction: It is the process to identify the Users which is likely to churn out, based on their usage. This will help many businesses to reduce churn and create strategies to win lost clients by understanding why customers leave and analyze the actual churn rate presented by the data in this paper. This study employs logistic ID3 decision tree, support vector machine and artificial neural network (ANN) machine learning techniques. The dataset used in this analysis is referred to as churn modeling. Kaggle dataset site The data set was hosted on Kaggle website The comparison of findings helps to determine the suitable of model more accurately. Thus the Random Forest was superior it terms of accuracy to the other algorithms. Also, the accuracy was about 88%. The outcome of the least accurate result was a decision tree algorithm with an accuracy of 79.4%