PORTFOLIO PROJECT USING SYNTHETIC DATA
LocalMotion Mileage Analysis
In development
A case study using simulated trip records to explore the difference between total and payable miles. It will use Python for exploratory analysis, R to examine an association, and Tableau to compare cohorts.
Python / R / Tableau
Business Question
How do total and payable miles differ across trip cohorts, and does an association help explain those differences?
Data and Scope
Simulated trip records inspired by LocalMotion Endorsements. The payable-mile rule and assumptions will be documented before analysis.
Validation
Planned evidence: test payable-mile business rules, investigate anomalies and missing values, and check that cohort totals reconcile with the trip-level data.
Investigation log template · populate with actual checks and resolutions
Issue
How I checked it
How I handled it
Duplicate
Add the key, query, and affected records.
Add the decision and audit trail.
Missing value
Add completeness checks and source review.
Add the recovery or exclusion rationale.
Reconciliation discrepancy
Add source-to-output count and amount checks.
Add the root cause and resolution.
Workflow
Planned workflow: explore trip distributions and descriptive statistics in Python; examine correlation or regression in R; compare segments and cohorts in Tableau.
Documentation
Planned documentation. Supporting files will appear when the project work is ready.
Data dictionary
KPI definitions
Business requirements
Source notes
Query notes
Assumptions and limitations
Refresh procedure