Analyze Walmart sales data to explore trends between store sales and unemployment rates. Generate insightful visualizations and a beautiful HTML report with deep analysis. Suitable for quick insights into the relationship between sales data and macroeconomic factors.
This skill is designed to help users conduct in-depth analysis of Walmart sales data, particularly exploring the relationship between sales and unemployment rates across different stores. It visually presents these trends by generating visualizations with detailed interpretations and professional HTML reports.
This skill provides the following analysis and visualization features:
To use this skill, you need to provide a CSV file containing Walmart sales data. The file should contain at least the following columns: Store (Store ID), Date (Date), Weekly_Sales (Weekly Sales), Unemployment (Unemployment Rate).
execute_skill_script_file tool to run the generate_html_report.py script. Pass the CSV file path to the input_file argument in the args parameter.
{"skill_name": "walmart-sales-analyzer", "script_file_name": "generate_html_report.py", "args": {"input_file": "/path/to/Walmart_Sales.csv", "output_dir": "."}}correlation_heatmap.png, sales_vs_unemployment_scatter.png, etc.) and the base report.html_interpreter tool. Provide the template_path (walmart-sales-analyzer/templates/report_template.html) and the necessary text data to render the report interactively. You MUST fill in ALL the placeholders dynamically based on your analysis (including ALL section titles, report titles, and analysis content, otherwise they will render as 'NA') and ensure they are translated to the user's language.
data payload:
{
"LANG": "en",
"REPORT_TITLE": "Walmart Sales Deep Analysis Report",
"REPORT_SUBTITLE": "Based on macroeconomic indicators and store performance",
"EXEC_SUMMARY_TITLE": "Executive Summary",
"EXEC_SUMMARY_CONTENT": "<p>Your detailed summary...</p>",
"SECTION_1_TITLE": "1. Multi-dimensional Correlation Analysis",
"SECTION_1_ANALYSIS": "<h3><span class="tag">Insights</span> Variable relationships</h3><ul><li>...</li></ul>",
"SECTION_2_TITLE": "2. Sales vs Unemployment Regression",
"SECTION_2_ANALYSIS": "<h3><span class="tag">Deep Dive</span> Resilience under pressure</h3><p>...</p>",
"SECTION_3_TITLE": "3. Dynamic Trends Tracking",
"SECTION_3_ANALYSIS": "<h3><span class="tag">Trends</span> Seasonal vs Macro</h3><p>...</p>",
"SECTION_4_TITLE": "4. Store Performance Comparison",
"SECTION_4_ANALYSIS": "<h3><span class="tag">Strategy</span> Regional operations</h3><p>...</p>",
"CONCLUSION_TITLE": "Final Conclusions & Recommendations",
"CONCLUSION_CONTENT": "<ol><li>...</li></ol>",
"FOOTER_TEXT": "Deep Data-Driven Decisions"
}
terminate with a final answer summarizing your actions.scripts/generate_html_report.py: Recommended, generates an HTML report containing all charts and deep analysis with one click.scripts/generate_correlation_heatmap.py: Generates a data correlation heatmap.scripts/generate_sales_unemployment_scatter.py: Generates a scatter plot of sales vs. unemployment rate.scripts/generate_time_series_trend.py: Generates a time series trend chart for a specific store.scripts/generate_store_avg_comparison.py: Generates a comparison chart of average values across stores.templates/report_template.html: HTML style template used to generate the deep analysis report.