Car Sales Data Engineering Analytics

reason-machines/data-skills/skills/car-sales-data-engineering-analytics

by reason-machines8e7bd5f22574No license4 starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated 2 months ago

Process, clean, and analyze car sales data with statistical modeling and interactive Streamlit dashboards for business insights.

Instructions onlyData & Analytics
AI-generated overview

Processes, cleans and statistically analyzes car sales data, producing charts and an interactive Streamlit dashboard.

What it does
The skill describes an ETL and analytics framework for roughly 24,000 car sales records. It covers loading and cleaning a CSV, computing descriptive and revenue statistics, running 15 predefined analyses such as price distributions, regional and monthly trends, t-tests, multiple linear regression, Z-score outlier detection and Shapiro-Wilk normality tests, and saving cleaned data plus PNG figures. It also documents a Streamlit dashboard with filtering, segment comparison and CSV export.
When to use it
Use it when working with an automotive sales dataset and needing cleaning, statistical modeling or business-oriented visualizations. It suits requests for pricing, regional, demographic or product-mix analysis and for building an interactive sales dashboard.
Requirements
Python 3.10+ with pandas, numpy, matplotlib, scipy and streamlit, managed with uv. It expects a car sales CSV file and writes cleaned data and figure outputs. The skill contains instructions only and ships no scripts.

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:

bash
# Clone the repositorygit clone https://github.com/Abdumalik-ProDev/Car-Sales-Data-Engineering.gitcd Car-Sales-Data-Engineering
# Install dependenciesuv sync

Dependencies: Python 3.10+, pandas, numpy, matplotlib, scipy, streamlit

Quick Start

Launch Interactive Dashboard

bash
# Start Streamlit web UIuv run streamlit run src/ui.py
# Alternative via entry pointuv run python -m src.main

Run Full Pipeline

bash
# Execute all 15 analyses and generate figuresuv run python -m src.main --pipeline

This will:

  • Load and clean data/Car sales.csv
  • Generate outputs/cleaned_data.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

python
from src.analysis import CarSalesAnalysis
# Initialize analyzeranalyzer = CarSalesAnalysis('data/Car sales.csv')
# Access cleaned datadf = analyzer.dataprint(f"Total records: {len(df)}")print(f"Columns: {df.columns.tolist()}")
# Save cleaned datasetanalyzer.save_cleaned_data('outputs/cleaned_data.csv')

Key Columns:

  • car_id, date, customer_name, dealer_name, company, model
  • year, price, body_style, transmission, color
  • dealer_no, dealer_region, phone, gender, annual_income

Statistical Summaries

python
# Get descriptive statisticsstats = analyzer.describe_data()print(stats)
# Revenue metricstotal_revenue = analyzer.data['price'].sum()avg_price = analyzer.data['price'].mean()median_price = analyzer.data['price'].median()
print(f"Total Revenue: ${total_revenue:,.0f}")print(f"Avg Price: ${avg_price:,.0f}")print(f"Median Price: ${median_price:,.0f}")

Generate Individual Analyses

python
# Q1: Price distributionanalyzer.plot_price_distribution(save_path='outputs/figures/q1_price_dist.png')
# Q2: Monthly sales trendanalyzer.plot_monthly_sales_trend(save_path='outputs/figures/q2_monthly_trend.png')
# Q3: Sales by regionanalyzer.plot_sales_by_region(save_path='outputs/figures/q3_regional_sales.png')
# Q6: Income vs Price regressionanalyzer.plot_income_vs_price(save_path='outputs/figures/q6_income_price.png')
# Q9: Automatic vs Manual transmission comparison (t-test)analyzer.compare_transmission_prices(save_path='outputs/figures/q9_transmission.png')

Statistical Modeling

python
# Q12: Multiple linear regression# Predicts price from year, annual_income, transmissionanalyzer.multiple_regression_analysis(save_path='outputs/figures/q12_regression.png')
# Q13: Detect outliers using Z-scoresanalyzer.detect_outliers_zscore(save_path='outputs/figures/q13_outliers.png')
# Q15: Test price normality with Shapiro-Wilkanalyzer.test_normality(save_path='outputs/figures/q15_normality.png')

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. ⚖️ Compare Segments - Side-by-side comparison with t-tests

Custom Filtering Example

python
# Users can filter via sidebar widgets# Example: Filter cars by price range and region
# In ui.py, the filter logic:filtered = analyzer.data.copy()
if price_range:    filtered = filtered[        (filtered['price'] >= price_range[0]) &         (filtered['price'] <= price_range[1])    ]
if selected_regions:    filtered = filtered[filtered['dealer_region'].isin(selected_regions)]
if selected_companies:    filtered = filtered[filtered['company'].isin(selected_companies)]
# Display and exportst.dataframe(filtered)st.download_button(    "Download CSV",    filtered.to_csv(index=False),    "filtered_sales.csv")

Common Analysis Patterns

Price Analysis by Category

python
# Average price by car companycompany_prices = analyzer.data.groupby('company')['price'].mean().sort_values(ascending=False)print(company_prices.head(10))
# Price by body stylebody_prices = analyzer.data.groupby('body_style')['price'].agg(['mean', 'median', 'count'])print(body_prices)
# Price by transmission typetrans_prices = analyzer.data.groupby('transmission')['price'].describe()print(trans_prices)

Regional & Temporal Analysis

python
# Sales volume by regionregional_sales = analyzer.data['dealer_region'].value_counts()print(regional_sales)
# Monthly revenue trendanalyzer.data['month'] = pd.to_datetime(analyzer.data['date']).dt.to_period('M')monthly_revenue = analyzer.data.groupby('month')['price'].sum()print(monthly_revenue)
# Year-over-year comparisonyearly_sales = analyzer.data.groupby('year').agg({    'price': ['sum', 'mean', 'count']})print(yearly_sales)

Statistical Tests

python
from scipy import stats
# Compare prices: Automatic vs Manual transmissionauto_prices = analyzer.data[analyzer.data['transmission'] == 'Automatic']['price']manual_prices = analyzer.data[analyzer.data['transmission'] == 'Manual']['price']
t_stat, p_value = stats.ttest_ind(auto_prices, manual_prices)print(f"T-statistic: {t_stat:.4f}, P-value: {p_value:.4f}")
# Correlation between income and pricecorrelation = analyzer.data['annual_income'].corr(analyzer.data['price'])print(f"Income-Price Correlation: {correlation:.4f}")

Configuration

File Paths

Default paths are defined in src/analysis.py:

python
# Customize data pathsanalyzer = CarSalesAnalysis('custom_path/sales_data.csv')
# Custom output directoryanalyzer.save_cleaned_data('custom_output/cleaned.csv')
# Figures directoryos.makedirs('custom_figures', exist_ok=True)analyzer.plot_price_distribution(save_path='custom_figures/prices.png')

Streamlit Configuration

Create .streamlit/config.toml for dashboard customization:

toml
[theme]primaryColor = "#FF4B4B"backgroundColor = "#FFFFFF"secondaryBackgroundColor = "#F0F2F6"textColor = "#262730"
[server]port = 8501headless = trueenableCORS = false

Running Full Pipeline Programmatically

python
from src.analysis import CarSalesAnalysisimport os
# Initializeanalyzer = CarSalesAnalysis('data/Car sales.csv')
# Create output directoriesos.makedirs('outputs/figures', exist_ok=True)
# Save cleaned dataanalyzer.save_cleaned_data('outputs/cleaned_data.csv')
# Generate all 15 analysesanalyses = [    ('q1_price_dist.png', analyzer.plot_price_distribution),    ('q2_monthly_trend.png', analyzer.plot_monthly_sales_trend),    ('q3_regional_sales.png', analyzer.plot_sales_by_region),    ('q4_gender_split.png', analyzer.plot_gender_distribution),    ('q5_income_region.png', analyzer.plot_income_by_region),    ('q6_income_price.png', analyzer.plot_income_vs_price),    ('q7_company_prices.png', analyzer.plot_avg_price_by_company),    ('q8_body_style.png', analyzer.plot_price_by_body_style),    ('q9_transmission.png', analyzer.compare_transmission_prices),    ('q10_colors.png', analyzer.plot_popular_colors),    ('q11_heatmap.png', analyzer.plot_body_transmission_heatmap),    ('q12_regression.png', analyzer.multiple_regression_analysis),    ('q13_outliers.png', analyzer.detect_outliers_zscore),    ('q14_dealer_prices.png', analyzer.plot_dealer_prices),    ('q15_normality.png', analyzer.test_normality),]
for filename, func in analyses:    func(save_path=f'outputs/figures/{filename}')    print(f"✓ Generated {filename}")

Troubleshooting

Missing Data Issues

python
# Check for missing valuesmissing = analyzer.data.isnull().sum()print(missing[missing > 0])
# Handle missing valuesanalyzer.data = analyzer.data.dropna(subset=['price', 'year'])analyzer.data['annual_income'].fillna(analyzer.data['annual_income'].median(), inplace=True)

Date Parsing Errors

python
# Ensure proper date formatanalyzer.data['date'] = pd.to_datetime(analyzer.data['date'], errors='coerce')analyzer.data = analyzer.data.dropna(subset=['date'])

Memory Issues with Large Datasets

python
# Load only required columnsusecols = ['price', 'company', 'body_style', 'dealer_region', 'year']df = pd.read_csv('data/Car sales.csv', usecols=usecols)
# Use dtype optimizationdf['price'] = df['price'].astype('float32')df['year'] = df['year'].astype('int16')

Streamlit Port Conflicts

bash
# Specify custom portuv run streamlit run src/ui.py --server.port 8502
# Or in configecho "[server]\nport = 8502" > .streamlit/config.toml

Key Insights Reference

  • Total Records: 23,906 sales
  • Revenue: $655.6M total
  • Pricing: $27,426 avg, $23,000 median
  • Top Body Style: SUV (27%)
  • Top Region: Austin (17%)
  • Premium Brand: Cadillac ($37,557 avg)
  • Demographics: 79% Male, 21% Female
  • Transmission: 53% Automatic, 47% Manual

Source and attribution

Source:reason-machines/data-skillsinskills/car-sales-data-engineering-analyticsat commit8e7bd5f

License: No license

Content belongs to its original authors. SourceWeft indexes it from a public repository.

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