How saad0508/sales-data-analysis works
This repository provides a sales analytics dashboard and a separate batch-analysis script. In the dashboard workflow, a user selects region, category, and segment filters; the app loads and parses the sales CSV, computes filtered KPIs and sales breakdowns, then presents interactive charts and a data table. The batch script prints summary statistics and business insights and saves some charts. The tree contains Data/train.csv, while Main.py requests data/train.csv with different capitalization; successful batch loading is therefore uncertain. The README is empty, so capabilities are based on the sampled source.
Main entry points: app.py Dimension filters · Main.py Batch analysis · train.csv Sales CSV · charts Saved charts
Components 16
Interactive dashboard
- Streamlit dashboard
app.py - Load and parse
app.py - Dimension filters
app.py - Sales KPIs
app.py - Sales trend
app.py - Category and region
app.py - Top products
app.py - Filtered data table
app.py
Batch analysis
- Batch analysis
Main.py - Dataset checks
Main.py - Sales summaries
Main.py - Batch charts
Main.py - Business insights
Main.py - Saved charts
charts
Sales data
- Sales CSV
Data/train.csv
Other components
- Dashboard user
Connections 15
- Dashboard user → Streamlit dashboard: uses
- Streamlit dashboard → Load and parse: loads data
- Load and parse → Sales CSV: reads
- Streamlit dashboard → Dimension filters: applies
- Dimension filters → Sales KPIs: computes
- Dimension filters → Sales trend: groups dates
- Dimension filters → Category and region: groups sales
- Dimension filters → Top products: ranks sales
- Dimension filters → Filtered data table: displays rows
- Batch analysis → Sales CSV: reads
- Batch analysis → Dataset checks: checks
- Batch analysis → Sales summaries: computes
- Batch analysis → Batch charts: plots
- Batch charts → Saved charts: saves
- Batch analysis → Business insights: derives