Labs for DataRobot University

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Labs for DataRobot University

DataRobot University provides self-paced content and labs to help you learn how to use DataRobot to solve data science business problems. Lessons related to the labs walk you through the problem-solving workflow in DataRobot.

The following describes each lab and the related lessons.

Fuel Efficiency Lab—In this lab, you will predict the fuel efficiency of a new car that has not yet been designed. This is a regression problem, so you will learn how to frame, set up, evaluate, and interpret predictions for a continuous target.

  • Lessons: Regression, What-if App, Model Evaluation, Insights

Salary Prediction Lab—In this lab, you'll use machine learning to determine how different survey responses predict developer salaries. Think of this in the context of a Human Resources department determining the salary of an individual based on the experience needed for the position.

  • Lessons: Regression, Messy Data (Lots of Categoricals and Text), Optimization Metrics

COVID Lab—In this lab, you'll use look-ahead modeling to classify US counties that are at high risk for developing COVID-19.

  • Lessons: Classification, Target Leakage, Multicollinearity, Thresholds, ROC Curve, Lead Scoring, COVID-19

Medical Fraud Lab—In this lab, you will predict fraud using a medical claims dataset. (You can find a detailed description of this use case in DataRobot Pathfinder.) To achieve this, you will approach this as a binary classification problem and optimize the profitability by adjusting the prediction threshold.

  • Lessons: Classification, Imbalanced Datasets, Target Leakage, Confusion Matrix, Profit Curve, Hotspots

Lead Scoring Lab—In this lab, you'll use lead scoring to predict the probability that a prospect will become a customer. To achieve this, we are going to use binary classification.

  • Lessons: Classification, Lead Scoring, Learning Curves and Dataset Size, Prediction Explanation Excel Analysis

HR Bias Lab—In this lab, you will use DataRobot to learn how ethical issues can impact your machine learning projects. We are going to do that by building an ML model that will eventually be free of gender bias.

  • Lessons: AI Ethics, Classification, HR Use cases

Churn I Lab—In this lab, you'll learn a basic approach to churn modeling using many of the tools available in DataRobot.

  • Lessons: Classification, Churn, Unit of Analysis, Aggregating Data, Confusion Matrix, Thresholding, Profit Curve

Churn II Lab—In this lab, you'll learn additional techniques to aggregate and join data for your churn model.

  • Lessons: Classification, Churn, Unit of Analysis, Aggregating Data, Feature Engineering in Excel

Credit Card Fraud Lab—In this lab, you'll learn to use Frequency-Severity modeling to classify fraudulent credit card transactions. To achieve this, we are going to use a regression target.

  • Lessons: Regression, Zero-Inflated Target, Frequency-Severity Modeling, Model Comparison, Dual Lift, Creating Blenders

Using Geospatial Data Lab—In this lab, you will use geospatial data and location AI with a housing dataset to predict house prices.

  • Lessons: Regression, Location AI, Geospatial Maps, Accuracy Over Space

Categorizing Customer Complaints Lab—In this lab, you will use machine learning and Natural Language Processing (NLP) to classify consumer complaints.

  • Lessons: Multiclass, NLP Auto-tuned models, Multiclass Lift Charts, Multiclass Confusion Matrix
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