Essays are crucial testing tools for assessing academic achievement of the students. Manual grading is still used even after the implementation of various automated grading systems. This process takes a significantly larger amount of time of the evaluator and is also a costly process. Our attempt in this project is to automate the process of essay grading and grade the essay in a similar way that a human grader would do. The project aims to build an AES using a data set of 13,000 essays from kaggle.com. We divided the essays into 6 different sets based on the context. We extracted features such as grammar errors, spelling errors, part of speech counts and length of the sentence. First, the words are tokenized in order to make it easier for the computer to understand. Random forest regression and classifier were used to learn from the features and generate the parameters for testing and validation. We used an averaged perceptron to select the best score. This project has a wide scope in different educational institutions and organizations. One of its applications can be seen in various standardized tests and large-scale examinations such as the SAT and GRE.