# Big Data Mining and Classification of Intelligent Material Science Data Using Machine Learning
## 1 Introduction  
Our study is based on Intelligent Material science data (Magnesium-Alloy, Mg-alloy), which is structured to be a lightweight alloy without compromising the strength of the material and is widely used across multiple domains, including the United States Armed Forces [1; 2; 3]. The Mg-alloy data is composed of different metal compositions and their processing parameter information involving different techniques such as casting, extrusion, rolling, forging [4; 5; 6; 7; 8; 9; 10; 11], etc. Currently, there is no existing data repository for storing Mg-alloy data. In this study, we propose a big data repository that archives the Mg-alloy data retrieved from scientific journals on which big data analytics, predictive modeling can be performed. The Mg-alloy tests are classified based on tensile strength properties including strength and the ductility of the metals. Based on these tensile properties, we propose the prediction model that will help in the future to determine the boundary conditions for strong Mg-alloy material for the specific purposes of an application. We will focus on how to address this material science research problem in creating a big data repository and predicting the strength of the material by applying Machine Learning (ML) models to the big data retrieved from the data repository. We have applied MongoDB for management of a Mg-alloy big database and a logistic regression algorithm for its binary classification [12; 13; 14]. MongoDB, a big data tool, for data management of Mg-alloy is chosen, as NoSQL (non-SQL, non-Structured Query Language or non-relational)databases like MongoDB are often better suited for storing and modeling semi-structured Mg-alloy data [15; 16; 17]. The Mg-alloy data is stored as documents on MongoDB, which is a popular NoSQL database [13]. The documents are queried from the Mongo collection to retrieve the tensile properties of the Mg-alloy as these properties of metals will help us to determine the strength of metals. Tensile is a mechanical testing method, through which tensile properties such as tensile yield strength (YS), ultimate tensile strength (UTS), and elongation-at-fracture (EL) of the material under testing can be obtained. These three tensile properties are independent of each other. Ductility of the metal depends on the tensile elongation-at-fracture (EL). The higher the elongation, the higher the ductility, and vice versa. Our goal is to find a statistical correlation between these three independent variables using machine learning (ML) predictions. Using ML techniques, ductility (a physical property) of the Mg-alloy is determined by considering the tensile yield strength (YS), ultimate tensile strength (UTS) as independent variables. Mg-alloy can form a strong or weak material, based on the combination of metal composition, its processing methods/parameters and tensile properties. In our methodology we are only considering tensile properties due to the sparse nature of the dataset in the variables of metal compositions, processing methods/parameters. Strong Mg-alloy is ductile metal and weak Mg-alloy is brittle metal. Mg-alloy can belong to either of these two classes. To address this classification problem, there are many possible popular Machine Learning classification algorithms, such as Support vector machine (SVM), Linear regression, K-nearest neighbor, Logistic regression, Random Forest, etc. [18; 19; 20; 21; 22; 23; 24]. However, choosing the best algorithm to solve a given problem depends on the number of factors; namely, accuracy of the model, data set formats, number of parameters, features, etc. Further, based on the above factors, nature of problem, presence of sparse dataset and the number of dependent and independent variables, the Logistic regression algorithm was chosen for Mg-alloy classification. The classification algorithms Support vector machine SVM and Random Forest are also applied on the independent variables to predict the output variable, ductility of the metal.