Sales-Data-Analysis

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.

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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

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Project Structure

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

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Setup

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

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Generate Synthetic Data

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

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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/fig_daily_revenue.png
• outputs/fig_monthly_revenue.png
• outputs/fig_category_revenue.png

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Sample Results

Daily Revenue

Monthly Revenue

Revenue by Category

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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