AxionRay Data Analysis

This folder contains data analysis scripts for the AxionRay assignment, focusing on vehicle defect and repair analysis.

Project Structure

data/
├── raw/               # Original Excel data files
└── processed/         # Processed CSV files

src/
├── task1/            # Scripts for Task 1 analysis
├── task2/            # Scripts for Task 2 analysis
└── charts/           # Chart generation scripts

outputs/
├── figures/          # Generated charts and visualizations
└── reports/          # Generated reports and findings

Setup

1. Install the required packages:

   pip install -r requirements.txt
   

2. Place your Excel data files in the data/raw/ directory.

3. Run the main analysis script:

   python axionray_complete_analysis.py
   

Usage

• axionray_complete_analysis.py: Main script that runs the complete analysis pipeline

• Individual task scripts are organized in their respective directories under src/

• Generated outputs are saved in the outputs/ directory

• Topics Breakdown by Category

• End-to-end data analysis solution for automotive defect data, implementing data validation, integration, and exploratory analysis using Python scientific stack. Processes Excel datasets, generates interactive visualizations, and produces executive stakeholder reports.

Core Technologies: python, pandas, numpy, matplotlib, seaborn, plotly, openpyxl Data Science Domains: data-analytics, data-science, exploratory-data-analysis, business-intelligence data-visualization, data-cleaning, data-integration, etl-pipeline Industry Focus: automotive-data, vehicle-maintenance, cost-analysis, failure-analysis Technical Skills: statistical-analysis, excel-data-processing, stakeholder-reporting

This project's focus is on automotive data analytics, it demonstrates my technical proficiency with Python data science tools, and emphasizes the end-to-end nature of the analysis pipeline from raw data to stakeholder reports.