{"id":26446558,"url":"https://github.com/245839/automobile-analysis","last_synced_at":"2026-05-20T10:47:27.054Z","repository":{"id":282598629,"uuid":"949094194","full_name":"245839/Automobile-Analysis","owner":"245839","description":"Analysis of data on imported cars to the USA performed in Python using libraries for data analysis in the Jupyter environment.","archived":false,"fork":false,"pushed_at":"2025-03-15T17:14:12.000Z","size":628,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-15T18:19:16.721Z","etag":null,"topics":["data-analysis","jupyter-notebook","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/245839.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-03-15T16:59:40.000Z","updated_at":"2025-03-15T17:14:51.000Z","dependencies_parsed_at":"2025-03-15T18:29:20.731Z","dependency_job_id":null,"html_url":"https://github.com/245839/Automobile-Analysis","commit_stats":null,"previous_names":["245839/automobile-analysis"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/245839%2FAutomobile-Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/245839%2FAutomobile-Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/245839%2FAutomobile-Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/245839%2FAutomobile-Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/245839","download_url":"https://codeload.github.com/245839/Automobile-Analysis/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244217953,"owners_count":20417677,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analysis","jupyter-notebook","python"],"created_at":"2025-03-18T12:18:34.051Z","updated_at":"2026-05-20T10:47:27.021Z","avatar_url":"https://github.com/245839.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Automobile-Analysis\nThis 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.  \n\n## Dataset Overview  \nThe dataset consists of three main types of information:\n- **Vehicle Specifications** – Various characteristics such as make, body style, engine type, fuel system, horsepower, and more.  \n- **Insurance Risk Rating** – A \"symboling\" score indicating the vehicle's risk level.\n- **Normalized Losses** – The relative average insurance loss per vehicle per year, adjusted for different car categories (e.g., sports cars, station wagons).\n\n## Project Objectives \u0026 Methodology  \n- **Data Cleaning \u0026 Preprocessing** – Handling missing values and standardizing formats.  \n- **Feature Engineering** – Splitting features into **numerical** and **categorical** variables.  \n- **Data Visualization** – Creating visual representations of key dataset insights.  \n- **Exploratory Data Analysis (EDA)** – Investigating distributions, correlations, and patterns.  \n- **Predictive Modeling** – Implementing two machine learning models:  \n  -- Linear Regression\n  -- Random Forest\n- **Hyperparameter Optimization** – Fine-tuning RF model to enhance performance.\n\n## How to View \u0026 Run the Project\nTo run this project, you will need:  \n- **Python 3.x** with required libraries installed.  \n- **Jupyter Notebook** (Recommended) or another editor that supports `.ipynb` files.  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F245839%2Fautomobile-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2F245839%2Fautomobile-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F245839%2Fautomobile-analysis/lists"}