Creating confusion matrices and ROC curves to evaluate model performance.
While the exact columns depend on the specific version, students typically analyze these features for classification or regression tasks:
Often related to severity or churn , such as "Crash Severity" or "Customer Churn". Siu0207_2019.zip
Cleaning null values and generating 10+ visualizations to find correlations.
Is this for a specific (like a churn prediction or accident severity model)? Creating confusion matrices and ROC curves to evaluate
The file is likely a specialized dataset used for data analytics and machine learning projects , particularly in academic settings like Western Governors University (WGU). It is frequently associated with Course D207 (Data-Driven Decision Making) , where students perform Exploratory Data Analysis (EDA) and predictive modeling. Core Features of the Dataset
💡 If you are using this for a course assignment, ensure you check for any "Data Dictionary" files inside the zip; these define every column (feature) and its measurement scale. To help you build the feature or model you need: Is this for a specific (like a churn
Demographic info, technical logs, or incident-based data (e.g., timestamps, location coordinates, event types).