Sepsis is a life-threatening condition caused by infection and subsequent overreaction by the immune system. Physicians effectively treat sepsis with early administration of antibiotics. However, excessive use of antibiotics on false positive cases cultivates antibiotic resistant bacterial strains and can waste resources while false negative cases result in unacceptable mortality rates. Accurate early prediction ensures correct, early antibiotic treatment; unfortunately, prediction remains daunting due to error-ridden electronic medical records (EMRs) and the inherent complexity of sepsis. We aimed to predict sepsis using only the first 24 and 36 hours of lab results and vital signs for a patient. We used the Medical Information Mart for Intensive Care III (MIMIC3) dataset to test machine learning (ML) techniques including traditional methods (i.e., random forest (RF) and logistic regression (LR)) as well as deep learning techniques (i.e., long short-term memory (LSTM) neural networks). We successfully created a data pipeline to process and clean data, identified important predictive features using RF and LR techniques, and trained LSTM networks. We found that our best performing traditional classifier, RF, had an Area Under the Curve (AUC-ROC) score of 0.696, and our LSTM networks did not outperform RF.