🚗 Used Car Data Analytics

Comprehensive data analysis and machine learning project for predicting and analyzing used car prices.

🔗 Live Application

Access the live dashboard here: Used Car Analysis Dashboard

📋 Project Overview

This project performs an in-depth analysis of a dataset containing over 6,000 used car records. It explores factors influencing car prices, brand distributions, and technical specifications, providing interactive visualizations and machine learning insights.

✨ Key Features

• 📊 Interactive Dashboard: A multi-tab Streamlit application for data exploration.
• 🔍 Brand Analysis: Detailed breakdown of top brands and their market value.
• 📈 Visualizations: Rich, interactive charts powered by Plotly and Seaborn.
• 🤖 ML Insights: Feature correlation analysis to understand price drivers.
• 🔌 REST API: A Flask-based API for programmatic data access.
• 🐳 Containerized: Docker support for easy deployment and scalability.

🚀 Quick Start

Local Setup

1. Clone the repository
2. Install dependencies:

   pip install -r requirements.txt

3. Run the Streamlit app:

   streamlit run app.py

Running with Docker

docker-compose up

📁 Repository Structure

• app.py: Main Streamlit application.
• api.py: Flask REST API.
• used_cars.csv: Dataset used for analysis.
• test_app.py: Automated tests for CI/CD.
• DEPLOYMENT_GUIDE.md: Detailed instructions for cloud deployment.

🛠️ Technologies Used

• Python (Pandas, NumPy, Scikit-learn)
• Visualizations (Matplotlib, Seaborn, Plotly)
• Web Frameworks (Streamlit, Flask)
• DevOps (Docker, GitHub Actions)