A key business driver for Big Data analytics adoption is the ability to identify new business opportunities by analyzing large and complex datasets, which can provide valuable insights for decision-making and strategy development.

Multinomial logistic regression should be used when the dependent variable has more than two categories (i.e., it is categorical with more than two possible outcomes). This differs from binary logistic regression, which is used when the dependent variable is binary (i.e., has two categories).

Hive converts SQL-like commands into execution plans that run as Tez, Spark, or MapReduce jobs on a Hadoop cluster, enabling users to query large datasets using a familiar query language.

The primary role of a business intelligence analyst on an analytics project is to provide business-domain expertise. They bridge the gap between the technical aspects of the project and the business requirements, ensuring that the data and analysis align with the business objectives.

Data conditioning in the data preparation phase refers to the process of cleaning the data, normalizing datasets, and performing transformations to ensure the data is in a suitable format for analysis or modeling.

Ambari, Oozie, and Zookeeper are tools used for cluster and workflow management in Hadoop. Ambari manages and monitors clusters, Oozie handles workflow scheduling, and Zookeeper coordinates distributed processes.

P(Milk∩Eggs) is the probability of both Milk and Eggs being bought together, which is the count for Milk and Eggs (110).

P(Milk)) is the probability of Milk being bought, which is the total for Milk (233) divided by the grand total (374).

P(Eggs) is the probability of Eggs being bought, which is the total for Eggs (150) divided by the grand total (374).

The value of the lift for the association rule Milk - > Eggs is approximately 1.18.

The formula for Naïve Bayes is:

For the renewal class, we are given:

● P(Class = Renewal) = 0.8 (since 80% renew the policy)

● P(Age > 65 years | Renewal) = 0.3

● P(Housing = Own | Renewal) = 0.9

● P(Renewal Month = August | Renewal) = 0.1

P(Renewal) = P(Renewal) × P(Age  >  65 years | Renewal) × P(Housing = Own | Renewal) × P(Renewal Month = August | Renewal)

P(Renewal) = 0.8 × 0.3 × 0.9 × 0.1 = 0.0216

The naïve Bayes conditional independence assumption is applied to simplify the probability calculations by assuming that the features are independent given the class label. This reduces the complexity of the model and makes computation feasible even with many features.

In ANOVA (Analysis of Variance), the null hypothesis is that all population means are equal to each other. The goal is to test whether there is evidence to reject this hypothesis, indicating that at least one population mean differs from the others.