With the rise of new and more powerful quantum computers, one of the biggest advances in the area of quantum computation has been quantum machine learning. Variational circuits are a new type of machine learning model specifically used for quantum computing, consisting of a quantum circuit with parameterized gates that embeds input data in Hilbert space and produces quantum measurements used for classification. We would like to determine the advantages and efficiency of this model compared to classical machine learning when given similar tests and benchmarks. Using PennyLane, an open-source quantum simulator framework, we will create models for image classification, data analysis and reinforcement learning to analyze variational circuits in these environments and their accuracy against their classical counterparts.