Commit

Author: CyberTokyo112

README.md

@@ -1,65 +1,135 @@
-# Vehicle Repairs Data Analysis
+# 🚗 Data Analysis Using Python: Vehicle Repairs EDA
 
-## 📌 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.
+![Vehicle Repairs Analysis](https://img.shields.io/badge/Vehicle%20Repairs%20Analysis-EDA-blue)
 
----
+Welcome to the **Data Analysis Using Python** repository! This project focuses on exploratory data analysis (EDA) of a vehicle repairs dataset. It uncovers patterns in repair types, costs, and vehicle platforms. This repository provides a comprehensive approach to data cleaning, insights extraction, and tag generation from free-text fields.
 
-## 📂 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_
+## Table of Contents
 
----
+- [Project Overview](#project-overview)
+- [Features](#features)
+- [Technologies Used](#technologies-used)
+- [Getting Started](#getting-started)
+- [Usage](#usage)
+- [Data Cleaning Process](#data-cleaning-process)
+- [Insights Extraction](#insights-extraction)
+- [Tag Generation](#tag-generation)
+- [Visualizations](#visualizations)
+- [Release Information](#release-information)
+- [Contributing](#contributing)
+- [License](#license)
 
-## 📊 Analysis Highlights
+## Project Overview
 
-### 🔍 Data Exploration
-- Examined dataset shape, data types, and missing values  
-- Generated descriptive statistics for numerical and categorical columns  
-- Identified unique values and frequent patterns  
+This project provides an in-depth analysis of vehicle repairs. By examining the dataset, we aim to identify trends and insights that can inform better decision-making in vehicle maintenance and repair services. The analysis includes various aspects such as:
 
-### 🧹 Data Cleaning
-- Replaced missing categorical values with `"Unknown"`  
-- Substituted corrupted characters with `"Corrupt Value"`  
-- Filled missing `TOTALCOST` values using `REPORTING_COST` for consistency  
+- **Repair Types**: Understanding the most common types of repairs.
+- **Costs**: Analyzing the cost distribution across different repairs.
+- **Vehicle Platforms**: Identifying which vehicle platforms incur higher repair costs.
 
-### 💡 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  
+## Features
 
----
-## 💾 Output Files
+- Comprehensive exploratory data analysis (EDA)
+- Data cleaning to ensure data quality
+- Insights extraction for actionable outcomes
+- Tag generation from free-text fields
+- Visualizations to present findings clearly
+- Saving of cleaned datasets for further analysis
 
-- `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  
+## Technologies Used
 
-## 📁 Files
+This project leverages several powerful libraries and tools:
 
-- `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)  
+- **Python**: The main programming language used for analysis.
+- **Pandas**: For data manipulation and analysis.
+- **NumPy**: For numerical operations.
+- **Matplotlib**: For creating static visualizations.
+- **Seaborn**: For statistical data visualization.
+- **Jupyter Notebook**: For an interactive coding environment.
+- **Counter**: For counting hashable objects.
 
----
+## Getting Started
+
+To get started with this project, clone the repository to your local machine. Use the following command:
+
+```bash
+git clone https://github.com/CyberTokyo112/data-analysis-using-python.git
+```
+
+Navigate to the project directory:
+
+```bash
+cd data-analysis-using-python
+```
+
+Install the required libraries:
+
+```bash
+pip install -r requirements.txt
+```
+
+## Usage
+
+To run the analysis, open the Jupyter Notebook file:
+
+```bash
+jupyter notebook vehicle_repairs_analysis.ipynb
+```
+
+Follow the instructions in the notebook to perform the analysis step by step.
+
+## Data Cleaning Process
+
+Data cleaning is crucial for ensuring the quality of analysis. In this project, we perform the following steps:
+
+1. **Handling Missing Values**: Identify and address missing data points.
+2. **Removing Duplicates**: Ensure unique entries in the dataset.
+3. **Standardizing Formats**: Normalize formats for dates, text, and numerical values.
+4. **Outlier Detection**: Identify and handle outliers that may skew results.
 
-## 🧠 Notes
-This analysis can support further research on repair cost optimization, predictive maintenance, and customer experience improvements.
+## Insights Extraction
+
+After cleaning the data, we extract insights to understand trends. Key insights include:
+
+- Most common repair types and their frequencies.
+- Average costs associated with different repair types.
+- Trends over time in repair requests.
+
+These insights can guide businesses in making informed decisions.
+
+## Tag Generation
+
+Generating tags from free-text fields helps in categorizing data. This project uses simple string processing techniques to create meaningful tags. For example, repairs described as "engine failure" may be tagged as "engine" and "failure."
+
+## Visualizations
+
+Visualizations play a vital role in presenting findings. The project includes various charts and graphs to illustrate insights, such as:
+
+- Bar charts showing the frequency of repair types.
+- Box plots displaying cost distributions.
+- Line graphs illustrating trends over time.
+
+These visualizations make it easier to understand complex data at a glance.
+
+## Release Information
+
+For the latest releases, please visit the [Releases section](https://github.com/CyberTokyo112/data-analysis-using-python/releases). You can download and execute the files available there.
+
+## Contributing
+
+We welcome contributions to improve this project. If you have suggestions or improvements, please follow these steps:
+
+1. Fork the repository.
+2. Create a new branch (`git checkout -b feature-branch`).
+3. Make your changes.
+4. Commit your changes (`git commit -m 'Add new feature'`).
+5. Push to the branch (`git push origin feature-branch`).
+6. Open a pull request.
+
+## License
+
+This project is licensed under the MIT License. See the LICENSE file for details.
 
 ---
+
+Thank you for checking out the **Data Analysis Using Python** repository! We hope this project helps you gain insights into vehicle repairs and enhances your data analysis skills. For more details, visit the [Releases section](https://github.com/CyberTokyo112/data-analysis-using-python/releases).