Agent skill · data analytics · aradotso
car-sales-data-engineering-analytics
Process, clean, and analyze car sales data with statistical modeling and interactive Streamlit dashboards for business insights.
Why this skill is useful
Adds a complete ETL pipeline and 15 pre-built statistical analyses for car sales data that the AI wouldn't generate on its own.
What it needs
Requires matplotlib, numpy, pandas, python, scipy, streamlit installed locally. About 4k tokens when loaded. Last updated 2026-08-05. 5 stars on the source repository.
What this skill does
Car Sales Data Engineering & Analytics Skill by ara.so — Data Skills collection. A comprehensive data engineering and analytics framework for processing ~24K car sales records with ETL pipelines, statistical modeling, and interactive Streamlit dashboards. Provides 15 pre-built analyses covering pricing trends, regional patterns, demographic insights, and feature correlations. Installation This project uses uv for package management: Dependencies: Python 3.10+, pandas, numpy, matplotlib, scipy, streamlit Quick Start Launch Interactive Dashboard Run Full Pipeline This will: Load and clean data/Car sales.csv Generate outputs/cleaneddata.csv Create 15 PNG charts in outputs/figures/ Core Module: src/analysis.py The main analysis engine provides ETL, statistics, and modeling capabilities. Data Loading & Cleaning Key Columns: carid, date, customername, dealername, company, model year, price, bodystyle, transmission, color dealerno, dealerregion, phone, gender, annualincome Statistical Summaries Generate Individual Analyses Statistical Modeling Streamlit Dashboard (src/ui.py) Page Structure The dashboard provides 6 interactive sections: 1. 📊 Overview - Data summary, sample rows, statistics 2. 💰 Sales & Revenue - Price trends, regional analysis 3. 👥 Demographics - Gender, income patterns 4. 🔧 Product Insights - Brand, body style, transmission 5. 📈 Statistical Modeling - Regression, outliers, normality 6. 🔍 Filter & Explore - Custom filters with CSV export 7. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aradotso/car-sales-data-engineering-analytics