List of Tables

Chapter 2. Should you send a purchase order to a technical approver?

Table 2.1. Technical Approval Required dataset contains information from prior orders received by Karen.

Table 2.2. Technical Approval Required dataset displayed in Excel

Table 2.3. Simple three-row dataset with prices for a desk, a keyboard, and a mouse

Table 2.4. The three-row dataset after applying the get_dummies function

Table 2.5. Correlation with predicted value

Table 2.6. Test results from the predictor

Chapter 3. Should you call a customer because they are at risk of churning?

Table 3.1. Test results from the predictor

Table 3.2. Customer dataset grouped by week after normalizing the data

Table 3.3. Customer dataset grouped by week, showing changes per week

Table 3.4. Dataset for Carlos’s customers displayed in Excel

Table 3.5. The transformed dataset without categorical information

Table 3.6. Results of the test

Chapter 4. Should an incident be escalated to your support team?

Table 4.1. Tweet dataset

Table 4.2. The top five rows in the tweet dataset

Table 4.3. Validation data for Naomi’s tweets

Chapter 5. Should you question an invoice sent by a supplier?

Table 5.1. Dataset invoice lines for the lawyers submitting invoices to the bank

Table 5.2. Dataset invoice lines display the three rows returned from running display(df[5:8]).

Table 5.3. Data before applying the get_dummies function

Table 5.4. Data after applying the get_dummies function

Chapter 6. Forecasting your company’s monthly power usage

Table 6.1. Time-series data showing my (Doug's) weight in kilograms over the past year

Table 6.2. Time-series data showing the weight in kilograms of family members over a year

Table 6.3. Power usage data sample for Kiara’s company in 30-minute intervals

Table 6.4. Power usage data in half-hour intervals

Table 6.5. Power usage data in daily intervals

Table 6.6. Predicting power usage data for site 1 of Kiara’s companies

Chapter 9. Case studies

Table 9.1. Data table showing candidate predictions