AxionRay Senior-Analyst Assessment - BLR

Company: AxionRay
Role Context: Evaluating proficiency in data validation, cleaning, integration, and exploratory data analysis using Python.
Domains: Automotive quality, failure diagnostics, and component analytics.

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Assignment Overview

AxionRay, a leading AI-driven engineering safety company, focuses on leveraging Large Language Models and Generative AI to improve data quality and operational insights for next-gen products like electric vehicles and airplanes.

This assignment consists of three main tasks, which were successfully completed and documented:

• Task 1: Data Validation & Cleaning
• Task 2: Data Integration & Preparation
• Task 3: Exploratory Data Analysis (Trend & Root Cause Analysis)

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Problem Statement

View Problem Statement (PDF)

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Task 1 – Data Validation & Tag Extraction

Goals

• Column-wise dataset profiling
• Handling missing & malformed data
• Tag extraction from free-text fields
• Exploratory visualizations

Key Actions

• Standardized column names
• Replaced nulls and dropped critical-missing rows
• Extracted tags from correction_verbatim via keyword-matching
• Identified system-level causes for missing causal_part_nm
• Highlighted top 10 most failing components

Outputs

• Bar plots of component failures
• Tag generation for free-text diagnostics
• Missing data insights (plant, dealer-level trends)

Files & Deliverables

• Original Dataset
• Cleaned Dataset with Tags
• Jupyter Notebook
• PDF Report
• Python Script

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Task 2 – Data Preparation & Integration

Objectives:

• Identify and justify a common primary key for dataset merging
• Perform thorough data cleaning: missing values, datatype corrections, standardizations
• Merge datasets with appropriate join strategy

Key Steps:

• Used "Primary Key" column for a left join
• Cleaned whitespace and standardized column names
• Filled missing values:
  • Coverage → "Unknown"
  • Cause, Correction → "Not Mentioned"
• Converted numeric columns to float where needed
• Removed duplicates and checked null distributions

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Task 3 – Exploratory Data Analysis (EDA)

3.1 Trend Analysis

• Transformed Order Date to datetime
• Grouped by month to analyze volume trends
• Visualized trends using:
  • Line Plot (monthly order count)
  • Heatmap (variable correlation)

3.2 Root Cause Identification

• Bar chart showing top 8 components contributing to revenue loss
• Boxplot comparing actual hours spent across failure components

Insights:

• Identified components with highest cost/time burden
• Discovered correlations that inform resource planning
• Visualization-driven exploration enables root cause prioritization

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Deliverables (Tasks 2 & 3 Combined)

• Problem Statement
• Original Dataset
• Merged Output Dataset
• Jupyter Notebook
• PDF Report
• Python Script

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Conclusion

The tasks demonstrate:

• Clear identification and resolution of data quality issues
• Integration of disparate datasets using relational keys
• Visualization-driven insights into component behavior and trends
• Strategic recommendations for data validation and cost optimization

These outputs can directly support AxionRay's mission to enhance quality engineering through AI and data-driven diagnostics.