Data Analysis & Rule-Based Recommendation Engine from Survey + Historical Data

We are working on analyzing survey data and historical system testing data to build a rule-based recommendation system for performance testing scope.

The project involves:

Comparing survey data sources
Analyzing historical data to identify patterns
Creating a rule-based logic for recommendations

Task Breakdown
Task 1: Survey Gap Analysis
Compare two sources of survey data:
JIRA-based intake survey
Pulse (UI / system) surveys
Identify:
Missing fields/questions
Differences in structure
Deliverable:
Excel or document highlighting gaps and recommendations

Task 2: Historical Data Analysis
Work with datasets containing:
System IDs
Survey responses (change indicators like hardware, software, DB, etc.)
Historical test execution data (e.g., Peak, Duration, Saturation tests)
Goal:
Identify patterns between changes and tests performed
Deliverable:
Summary of patterns
Grouped analysis (e.g., combinations of changes → test type)

Task 3: Rule-Based Recommendation Logic
Based on patterns identified:
Create a rule-based system such as:
If Hardware Change → Recommend Peak Test
If DB + Software Change → Recommend Duration Test
Deliverable:
Documented rules
Optional: Python or SQL-based implementation

Expected Skills
Strong experience in Data Analysis
Good knowledge of SQL (grouping, aggregation)
Experience with Python (Pandas preferred)
Ability to identify patterns and build logic
Experience with Excel / data comparison