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Predictive Analytics brings together advanced analytics capabilities spanning ad-hoc statistical analysis, predictive modeling, data mining, text analytics, entity analytics, optimization, real-time scoring, machine learning and more. IBM SPSS Modeler puts these capabilities into the hands of business users, data scientists, and developers. In this course in the Big Data University you will learn the basics to get started with Predictive Modeling.

**Module 1: Introduction to Data Mining**

**Question: Which of the following applications would require the use of data mining? Select all that apply.**

- Predicting the outcome of flipping a fair coin
- Determining which products in a store are likely to be purchased together
- Predicting future stock prices using historical records
- Determining the total number of products sold by a store
- Sorting a student database by gender

**Question: Which of the following is NOT a section of the Modeler Interface?**

- Nodes
- Palettes
- Stream, Outputs, and Model Manager
- Stream Canvas
**All of the above are sections of the Modeler Interface**

**Question: Which of the following is NOT a part of the Cross-Industry Process for Data Mining?**

- Data Storage
- Evaluation
- Business Understanding
- Data Preparation
- Modeling

**Module 2: The Data Mining Process**

**Question: Which phase of the data mining process focuses on understanding the project requirements and objectives?**

**Business Understanding**- Data Understanding
- Data Preprocessing
- Data Preparation
- Data Exploration

**Question: Which Data Preprocessing task focuses on removing outliers and filling in missing values?**

**Data Cleaning**- Data Integration
- Data Transformation
- Data Reduction
- None of the above

**Question: The IBM SPSS Modeler supports which data type?**

- Continuous
- Categorical
- Ordinal
- Nominal
- All of the above

**Module 3: Modeling Techniques**

**Question: Which of the following methods are commonly used for supervised learning tasks? Select all that apply.**

- Neural Networks
- Decision Trees
- K-Means
- CARMA
- Regression

**Question: Classification is a subset of supervised learning that focuses on modeling continuous variables. True or false?**

- True
**False**

**Question: Which of the following algorithms is NOT supported by the SPSS Modeler?**

- Logistic Regression
- Apriori
- K-Means
- CARMA
**All of the above algorithms are supported**

**Module 4: Model Evaluation**

**Question: What is the term for a negative data point that is incorrectly classified as positive?**

- False Positive
- False Negative
- True Positive
- True Negative
- None of the above

**Question: Which of the following is NOT a cost-sensitive performance metric?**

- Sensitivity
- Precision
- Specificity
- Accuracy
- All of the above metrics are cost-sensitive

**Question: What is the formula for the precision metric?**

- (True Negative) / (True Negative + False Positive)
- (True Positive) / (True Positive + False Negative)
- (False Positive) / (True Positive + False Positive)
- (False Positive) / (True Negative + True Positive)
**(True Positive) / (True Positive + False Positive)**

**Module 5: Deployment on IBM Bluemix**

**Question: In general, the testing dataset should be significantly larger than the training dataset. True or false?**

- True
**False**

**Question: Which of the following is NOT a model deployment solution?**

- CRISP-DM
- Bluemix
- IBM Collaboration and Deployment Services
- SPSS Solution Publisher
- All of the above are model deployment solutions

**Question: Which of the following statements are true of IBM Bluemix? Select all that apply.**

- Bluemix generally takes about a week to deploy an app
- Bluemix is supported by a growing community
- Bluemix is closed-source
- Bluemix provides a self-service application-hosting environment
- Bluemix provides built-in load-balancing capabilities

**Final Exam**

**Question: Which of the following suggests that the model is overfitting the data?**

- High accuracy on training data and high accuracy on testing data
- High accuracy on training data and low accuracy on testing data
- Low accuracy on training data and low accuracy on testing data
- Low accuracy on training data and high accuracy on testing data
- None of the above

**Question: Which of the following tasks would require the use of data mining?**

- Predicting the outcome of rolling two fair dice
- Computing the number of products sold over a given time period
- Determining which products in a store are likely to be purchased together
- Sorting a customer database by age
- All of the above

**Question: Suppose you have collected data on your customers and you wish to determine the demographics they fall into. Which technique is best suited for this task?**

- Decision Tree
- Neural Network
- Logistic Regression
- Linear Regression
- Clustering

**Question: Suppose you wish to use data mining in order to determine which customers are most likely to sign up for a new service. Which technique is best suited for this task?**

- CARMA
- Decision Tree
- Apriori
- Sequence
- K-means

**Question: Which SPSS Modeler node can be used to determine a model’s performance? Select all that apply.**

- Evaluation Node
- Analysis Node
- Table Node
- Auto Classifier Node
- Sequence Node

**Question: Which of the following is NOT a classification or prediction algorithm in SPSS Modeler?**

- Apriori
- Discriminant
- Logistic Regression
- Linear Regression
- Neural Network

**Question: Which SPSS Modeler node is used to specify whether a given field is an input or a target?**

- Type Node
- Analysis Node
- Table Node
- Data Audit Node
- Auto Classifier Node

**Question: Which SPSS Modeler node is useful for exploratory analysis on a data set?**

- Auto Classifier Node
- Evaluation Node
- Table Node
- Analysis Node
- Data Audit Node

**Question: Which SPSS Modeler node is used to both rename fields and exclude fields from the model?**

- Partition Node
- Evaluation Node
- Data Audit Node
- Filter Node
- Restructure Node

**Question: What is the formula for the accuracy metric? TP = true positive, TN = true negative, FP = false positive, and FN = false negative.**

**(TP + TN) / (TP + TN + FP + FN)**- TP / (TP + FP)
- (FP + FN) / (TP + TN + FP + FN)
- TN / (TN + FP)
- TP / (TP + FN)

**Question: Which major data preprocessing step focuses on feature selection and feature extraction?**

- Data Integration
- Data Audit
- Data Transformation
- Data Reduction
- Data Cleaning

**Question: Which SPSS Modeler node is used to identify missing data and screen out potentially problematic fields?**

- Restructure Node
- Data Audit Node
- Evaluation Node
- Auto Classifier Node
**Auto Data Preparation Node**

**Question: SPSS Modeler provides automated tools that determine the best algorithm to use for an application. True or false?**

**True**- False

**Question: Which SPSS Modeler node is used for sampling the data set?**

- Type Node
- Filter Node
- Data Audit Node
- Restructure Node
- Partition Node

**Question: Which phase of the data mining process focuses on gathering insights about the data set?**

- Business Understanding
- Data Preparation
- Data Understanding
- Data Preprocessing
- Data Integration

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