🚗 Data Analysis Using Python: Vehicle Repairs EDA

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.

Table of Contents

• Project Overview
• Features
• Technologies Used
• Getting Started
• Usage
• Data Cleaning Process
• Insights Extraction
• Tag Generation
• Visualizations
• Release Information
• Contributing
• License

Project Overview

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:

• 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.

Features

• 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

Technologies Used

This project leverages several powerful libraries and tools:

• 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:

git clone https://github.com/CyberTokyo112/data-analysis-using-python.git

Navigate to the project directory:

cd data-analysis-using-python

Install the required libraries:

pip install -r requirements.txt

Usage

To run the analysis, open the Jupyter Notebook file:

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.

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. 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.

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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.