**is the foundation for today’s insights on customer, products, costs and revenues which learns from the data provided to its algorithms.**

__Machine Learning__

**Decision Trees**: Decision tree output is very easy to understand even for people from non-analytical background. It does not require any statistical knowledge to read and interpret them. Fastest way to identify most significant variables and relation between two or more variables. Decision Trees are excellent tools for helping you to choose between several courses of action. Most popular decision trees are CART, CHAID, and C4.5 etc.

**Logistic Regression**: Logistic regression is a powerful statistical way of modeling a binomial outcome with one or more explanatory variables. It measures the relationship between the categorical dependent variable and one or more independent variables by estimating probabilities using a logistic function, which is the cumulative logistic distribution.

**Support Vector Machines**: Support Vector Machine (SVM) is a supervised machine learning technique that is widely used in pattern recognition and classification problems - when your data has exactly two classes.

**Naive Bayes**: It is a classification technique based on Bayes’ theorem and very easy to build and particularly useful for very large data sets. Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods. Naive Bayes is also a good choice when CPU and memory resources are a limiting factor

**and text classification**

__Sentiment analysis__

**Apriori**: This algorithm generates association rules from a given data set. Association rule implies that if an item A occurs, then item B also occurs with a certain probability.

**Random Forest**: is an ensemble of decision trees. It can solve both regression and classification problems with large data sets. It also helps identify most significant variables from thousands of input variables.

__high risks__**in manufacturing**

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__Deep Learning__**age, most businesses will tap into machine learning algorithms for their operational and customer-facing functions**

__Digital Transformation__