DC-Den

Terminologies We learnt a few machine learning  terminologies  and algorithms in this blog. Supervised  means we rely on labelled training data. It is task driven to identify a goal. Unsupervised  means unlabeled training data. It is data driven to identify a pattern. Classification arranges data into classes/categories using a labeled dataset. Regression develops a model to predict continuous numerical values. Clustering  separates an unlabeled dataset into clusters/groups of similar objects. Classification is a supervised learning algorithm, while Clustering is an unsupervised algorithm. Regression is considered supervised learning because the model is trained using both the input features and output labels - which can be numerical values. I will mention here that two other unsupervised approaches are:  Association , to identify underlying relationships, and Dimension Reduction , to reduce the number dimensions/features to make calculations simpler. I did not cover any methods on a

You conducted your survey and collected your data. You plotted your charts and suspect a certain conclusion. Which test should you use to validate your hypothesis? Having learnt different statistical tools and showing their implementation in Excel LAMBDA, we want a reference to guide us. Qualitative and Quantitative Data The first is to distinguish the type of data. Data can be qualitative or quantitative . Qualitative data can be nominal or ordinal. Nominal data are named categorised data, for example gender:{male, female} and direction:{north, south, east, west}. Ordinal data are categories with implied order, for example size:{small, medium, large}, school grades:{A, B, C, D, E} and service rating:{very poor, poor, average, good, great}. The order however are not equally spaced, i.e. you cannot equate the difference between poor and average, with the difference between average and good. Quantitative data can be interval or ratio. Unlike qualitative data, quantitative data are equal