Our Research paper proposes a model of travel time based prediction using regression techniques. The objective of our model is to predict the accurate trip duration of a taxi from one of the pickup location to another dropoff location. In today’s fast-paced world, where everyone is short of time and is always in a hurry, everyone wants to know the exact duration to reach his/her destination to carry ahead of their plans. So, for their serenity, we already have million dollar startups such as Uber and Ola where we can track our trip duration. As a result of this, we proposed a technique in which every cab service provider can give exact trip duration to their customers taking into consideration the factors such as traffic, time and day of pickup. So, in our methodology, we propose a method to make predictions of trip duration, in which we have used several algorithms, tune the corresponding parameters of the algorithm by analyzing each parameter against RMSE and predict the trip duration. To make our prediction we used RandomForest Regressor, LinearSVR and LinearRegression. We improved the accuracy by tuning hyperparameters and RandomForest gave the best accuracy. We also analyzed several data mining techniques to handle missing data, remove redundancy and resolve data conflicts. We used the NYC Limousine OpenData, and the travel details of the month of January in the year 2015 to carry ahead with feature extraction and prediction.