Data Science Project

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Company Name - Genpact Role: Data Scientist Topics: euclidian distance, normalisation vs standardization, ensemble learning, batch vs SGD, root cause analysis Q.   How will you measure the Euclidean distance between the two arrays in NumPy? A.  The magnitude or length of the line segment between two locations is known as the Euclidean distance in mathematics. We first create two numpy arrays in the first procedure. The Euclidean distance is then computed directly using numpy's linalg.norm() function. Q.  Difference between Normalisation and Standardization? A.   Normalization is the process of rescaling values into a range of [0,1]. Typically, standardisation entails rescaling data to a mean of 0 and a standard deviation of 1. (unit variance). Q.  What is Ensemble Learning? A.  Ensemble learning is the process of systematically generating and combining many models, such as classifiers or experts, to tackle a specific computational intelligence problem. Ensemble learning is largely used to improve a model's performance (classification, prediction, function approximation, etc.) or to lessen the risk of an unintentional poor model selection. Q. Difference between Batch and Stochastic Gradient Descent? A.  Batch Gradient Descent is highly slow on very big training sets since it entails calculations over the entire training set at each step. As a result, Batch GD becomes extremely computationally expensive. SGD is stochastic in nature, which means it chooses up a "random" instance of training data at each step and then computes the gradient, which is significantly faster than Batch GD because there is much less data to modify at once. Q.   What is root cause analysis? A.  A root cause is a component that contributed to a nonconformance and should be eradicated permanently through process improvement. The root cause is the most fundamental problem—the most fundamental reason—that puts in motion the entire cause-and-effect chain that leads to the problem (s). Root cause analysis (RCA) is a word that refers to a variety of approaches, tools, and procedures used to identify the root causes of problems. Some RCA approaches are more directed toward uncovering actual root causes than others, while others are more general problem-solving procedures, and yet others just provide support for the root cause analysis core activity.

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