Create README.md

Author: mudiittk

README.md

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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.
+
+---