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Vehicle Repairs Data Analysis

📌 Overview

This repository contains an Exploratory Data Analysis (EDA) of a vehicle repairs dataset. The dataset consists of 100 records and 52 columns, capturing detailed information about vehicle repair transactions. The goal is to uncover trends, frequent issues, and potential improvements based on the data.

📂 Dataset Description

  • File Name: vehicle_repairs.csv
  • Records: 100
  • Columns: 52

🔑 Key Columns

  • VIN: Vehicle Identification Number
  • TRANSACTION_ID: Unique repair transaction ID
  • CORRECTION_VERBATIM: Description of the repair done
  • CUSTOMER_VERBATIM: Customer's issue description
  • REPAIR_DATE: Date of repair
  • CAUSAL_PART_NM: Part responsible for the issue
  • GLOBAL_LABOR_CODE_DESCRIPTION: Type of repair performed
  • PLATFORM: Vehicle platform (e.g., Full-Size Trucks, BEV)
  • BODY_STYLE: Body style (e.g., Crew Cab, 4 Door Utility)
  • REPORTING_COST, TOTALCOST, LBRCOST: Cost-related metrics
  • ...and many more

📊 Analysis Highlights

🔍 Data Exploration

  • Examined dataset shape, data types, and missing values
  • Generated descriptive statistics for numerical and categorical columns
  • Identified unique values and frequent patterns

🧹 Data Cleaning

  • Replaced missing categorical values with "Unknown"
  • Substituted corrupted characters with "Corrupt Value"
  • Filled missing TOTALCOST values using REPORTING_COST for consistency

💡 Key Insights

  • Common Repairs: Steering wheel-related issues were most frequent
  • Platform Trends: Full-Size Trucks had the highest repair count
  • Cost Distribution: Repair costs varied significantly; a few were high-cost outliers
  • Geographical Patterns: Most repairs occurred in the US, especially in CA and TX

💾 Output Files

  • cleaned_steering_repair_data.csv: Final cleaned version of the dataset after preprocessing
  • generated_repair_tags.csv: Extracted tags from CORRECTION_VERBATIM and CUSTOMER_VERBATIM columns for downstream use

📁 Files

  • eda.ipynb: Jupyter notebook containing all EDA steps—loading, cleaning, exploration, and visualization
  • vehicle_repairs.csv: The original dataset in csv format
  • cleaned_vehicle_repairs.csv: Cleaned dataset with consistent and processed values
  • generated_tags.csv: Tags generated from free-text fields (correction/customer verbatim)

🧠 Notes

This analysis can support further research on repair cost optimization, predictive maintenance, and customer experience improvements.