Week 2 - Assignment - BUS625

Data Mining 1.What is the method name to gain insight into the data that involves central tendency, dispersion, and graphical displays of data? a.Basic statistical data description b.Data visualization c.Proximity measures d.Boxplot

Exploratory data analysis (EDA) is frequently preferred by analysts over hypothesis testing when it comes to data mining due to its greater efficiency.

Categorical and quantitative data have a lot of similarities.

Which of the following statement(s) is/are correct for boosting technique? A) Weight of observation, to be selected while building the next weak learner, increases if the observation is correctly classified B) Weight of observation, to be selected while building the next weak learner, increases, if the observation is incorrectly classified C) Weight of observation, to be selected while building the next weak learner,  decreases if the observation is correctly classified D) Weight of observation, to be selected while building the next weak learner,  decreases if the observation is incorrectly classified

Explain the concept of data modeling patterns and anti-patterns. Provide examples of common data modeling anti-patterns and how to avoid them.

k-Nearest Neighbor

The Frequentist approach looks at   Question 17 options:   the long-term relative frequency of events occurring   both single events occurring and the long-term relative frequency of events occurring   a single event occurring    neither single events occurring nor the long-term relative frequency of events occurring For data that is approximately normally distributed, any observation more than 1 standard deviation away from the mean is an outlier.   Question 26 options:   True   False

Because of its greater efficiency, exploratory data analysis (EDA) is often favoured by analysts over hypothesis testing when it comes to data mining.

In order to explain or understand the information that a prediction model provides, what approaches do you use?

What are the properties of covariance?

Contrast the terms data dependence; data independence

Explain how Logistic Regression works. Note: Please do it with your own words. Thankyou

7. How are covariance and correlation different from one another?

Big Data is often characterized by the three "V's," which describe its key attributes:

How can you tell if a model meets the requirements for proportionality and additivity?

Describe why it is important to evaluate a data model

Hi MATLAB code please

Solve and Illustrate the venn Diagram for each problem statement. 1. In a survey of 260 college students, the following data were obtained: 64 had taken a mathematics course, 94 had taken a computer science course, 58 had taken a business course, 28 had taken a M and B course, 26 had taken a M and CS course, 22 had taken a both CS and B course, 14 had taken a all three course. How many students were surveyed who had taken none of the three course?   2.. A survey of 500 television watchers produced the following information: 285 watched football games, 195 watched hockey games, 115 watch basketball games,,45 watch F & B, 70 watch F & H, 50 watch H & B, 50 do not watch any of the three games How many watched football only?

When building a data model, why is it sometimes preferable to leave out certain information?

Explain the following data analysis techniques with an example, diagram, applications, and pros and cons 1. Regression Analysis

What exactly is an Analysis Assumption?

When assessing the accuracy of a logistic regression model, the percent of incorrectly classified observations out of the total observations in the validation or test data is called the:    Question 14 options:   Accuracy   Overall error rate   Class   Error

CHLOE GALLIEN Part A How are the points of view of the mayor in "Assignment Day" and Skal in "Data Transfer" similar? They both believe that the goal of the a individual is to meet the demands of society. They both believe that some parts of the D population are more knowledgeable than others, They both believe that the wise provide protection for the innocent. They both believe that the most important aspects in life are made up of basic needs.

Model evaluation Create a predictions variable using your fitted model and the test dataset; call it y_pred. Then get the accuracy score of your predictions and save it in a variable called accuracy. Finally get the confusion matrix for your predictions and save it in a variable called confusion_mat. Code: y_pred = Noneaccuracy = Noneconfusion_mat = None

Data flow diagram (DFD) smoothing and data process modelling are defined.