/
Browse
/
Courses
/
Developing AI Models with the Python Machine Learning Client for SAP HANA | DE
/
Datenanalyse und Vorhersagemodellierung durchführen
Datenanalyse und Vorhersagemodellierung durchführen
Grundlagen von Datenanalyse und Vorhersagemodellierung verstehen
10 min
Klassifizierungs-, Regressions- und Zeitreihenanalyse anwenden
10 min
Quiz
Navigation durch maschinelles Lernen: Überwacht vs. Unüberwachte und Workflow-Implementierung
Unterscheidung zwischen überwachtem und unüberwachtem Lernen
10 min
Workflow für maschinelles Lernen implementieren
10 min
Quiz
Datenanalyse und Vorhersagemodellierung durchführen
Grundlagen von Datenanalyse und Vorhersagemodellierung verstehen
10 min
Klassifizierungs-, Regressions- und Zeitreihenanalyse anwenden
10 min
Quiz
Navigation durch maschinelles Lernen: Überwacht vs. Unüberwachte und Workflow-Implementierung
Unterscheidung zwischen überwachtem und unüberwachtem Lernen
10 min
Workflow für maschinelles Lernen implementieren
10 min
Quiz
Knowledge quiz
It's time to put what you've learned to the test, get 5 right to pass this unit.
1.
Which of the following scenarios would benefit most from classification techniques in predictive modeling?
Choose the correct answer.
Predicting stock prices based on historical data
Forecasting future sales based on past revenue data
Estimating the average temperature for the upcoming week
Identifying fraudulent transactions in a banking system
2.
You are a Data Scientist working for a healthcare provider. The organization wants to predict patient re-admissions to improve patient care and reduce costs. You have access to a dataset that includes patient demographics, medical history, treatments received, and previous hospital re-admissions. Your objective is to build a predictive model that can forecast which patients are at high risk of readmission within 30 days of discharge. Given the scenario provided, which steps would you take to define the concept of data analysis and predictive modeling in your project? Select all that apply.
There are two correct answers.
Analyze historical patient data to identify patterns and risk factors associated with re-admissions
Use the dataset to randomly assign re-admission probabilities to patients
Build a predictive model to forecast future re-admissions based on historical data
Ignore medical history and focus only on patient demographics
3.
Which of the following best describes the process of predictive modeling?
Choose the correct answer.
Creating a static report on past data
Building a statistical model to make future predictions based on historical data
Gathering and storing data without analysis
Visualizing data trends without further analysis
4.
You are a Data Analyst at a logistics company that has been experiencing significant delays in delivery times. Your manager asks you to investigate the root causes of these delays and to propose data-driven strategies to improve the efficiency of the delivery process. You have access to a wealth of data, including delivery times, routes, vehicle maintenance records, driver performance metrics, and customer feedback. Given the scenario provided, how can data help you in the decision-making process to improve delivery times? Select all that apply.
There are two correct answers.
Making decisions based on intuition and guesswork
Identifying patterns and trends in delivery times and routes
Ignoring customer feedback and focusing solely on internal metrics
Analyzing vehicle maintenance records to determine if there is a correlation with delays
5.
Why is linear regression useful in predictive modeling? (Select all that apply)
There are two correct answers.
It helps to classify data into distinct categories
It helps to estimate the relationship between dependent and independent variables
It helps to forecast future events based on historical data
It helps to identify outliers in the dataset
6.
In which scenario would time series analysis techniques be most appropriate for predictive modeling?
Choose the correct answer.
Forecasting future demand for a product based on time series data
Predicting customer churn in a telecommunications company
Estimating the probability of default for a loan applicant
Identifying spam emails in a mailbox