https://github.com/245839/automobile-analysis

Analysis of data on imported cars to the USA performed in Python using libraries for data analysis in the Jupyter environment.
https://github.com/245839/automobile-analysis

data-analysis jupyter-notebook python

Last synced: about 1 month ago
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Analysis of data on imported cars to the USA performed in Python using libraries for data analysis in the Jupyter environment.

• Host: GitHub
• URL: https://github.com/245839/automobile-analysis
• Owner: 245839
• License: mit
• Created: 2025-03-15T16:59:40.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-03-15T17:14:12.000Z (over 1 year ago)
• Last Synced: 2025-03-15T18:19:16.721Z (over 1 year ago)
• Topics: data-analysis, jupyter-notebook, python
• Language: Jupyter Notebook
• Homepage:
• Size: 613 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Automobile-Analysis

This project involves **data analysis of automobiles**, focusing on vehicle specifications, insurance risk ratings, and normalized loss values. The goal is to explore trends, identify patterns, and extract meaningful insights from the dataset.  

## Dataset Overview  

The dataset consists of three main types of information:

- **Vehicle Specifications** – Various characteristics such as make, body style, engine type, fuel system, horsepower, and more.  

- **Insurance Risk Rating** – A "symboling" score indicating the vehicle's risk level.

- **Normalized Losses** – The relative average insurance loss per vehicle per year, adjusted for different car categories (e.g., sports cars, station wagons).

## Project Objectives & Methodology  

- **Data Cleaning & Preprocessing** – Handling missing values and standardizing formats.  

- **Feature Engineering** – Splitting features into **numerical** and **categorical** variables.  

- **Data Visualization** – Creating visual representations of key dataset insights.  

- **Exploratory Data Analysis (EDA)** – Investigating distributions, correlations, and patterns.  

- **Predictive Modeling** – Implementing two machine learning models:  

  -- Linear Regression

  -- Random Forest

- **Hyperparameter Optimization** – Fine-tuning RF model to enhance performance.

## How to View & Run the Project

To run this project, you will need:  

- **Python 3.x** with required libraries installed.  

- **Jupyter Notebook** (Recommended) or another editor that supports `.ipynb` files.