Sales Data Analysis

Synthetic sales data analysis with Python. Generate realistic sales transactions, clean and validate data, compute KPIs, and visualize revenue trends by day, month, and category. Includes reproducible scripts and charts for portfolio demonstration. Simple end-to-end sales analytics: synthetic data generation, cleaning, KPIs, and charts.

Features

Generate synthetic daily orders
Clean and validate data (deduplicate, impute, recompute revenue)
Compute KPIs: daily/monthly revenue, average basket, growth rate
Visualize revenue trends with Matplotlib
Save outputs to the outputs/ directory

Project Structure

sales-data-analysis/
├─ README.md
├─ requirements.txt
├─ data/
│  └─ generate_sales.py
├─ src/
│  ├─ analyze_sales.py
│  └─ utils.py
└─ outputs/
   └─ figures & reports

Setup

python -m venv .venv
Windows:
.venv\Scripts\activate
macOS/Linux:
source .venv/bin/activate
pip install -r requirements.txt

Generate Synthetic Data

python data/generate_sales.py --start 2023-01-01 --end 2024-12-31 --seed 42 --out data/sales.csv

Run Analysis

python src/analyze_sales.py --input data/sales.csv --outdir outputs

The script produces KPIs and charts automatically.

Outputs

outputs/kpis.txt – main KPIs
outputs/figdailyrevenue.png
outputs/figmonthlyrevenue.png
outputs/figcategoryrevenue.png

Sample Results

Daily Revenue

Monthly Revenue

Revenue by Category

Data Schema

| column | description | |--------------|-----------------------------| | date | order date | | order_id | unique order identifier | | customer_id | customer identifier | | category | product category | | price | unit price (after discount) | | quantity | order quantity | | revenue | price * quantity |

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