🛒 Exploratory Data Analysis: Supermarket Sales

This repository is part of my Data Science / Machine Learning learning journey with Python.
It demonstrates how to set up a clean, reproducible project in GitHub, configure virtual environments, and perform an initial Exploratory Data Analysis (EDA) on the Sample Superstore dataset using pandas, matplotlib, and seaborn.

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📂 Repository Structure

Python-Data-Science-ML/ │ ├── data/ # local datasets (NOT pushed to GitHub, only .gitkeep placeholder) ├── notebook_py/ # analysis scripts in .py format │ └── test_env.py # environment test script │ └── eda_supermarket.py # main EDA script ├── figs/ # exported charts from EDA ├── requirements.txt # project dependencies └── README.md

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📊 Dataset

• Source: Kaggle — Sample Superstore
• Size: ~10,000 rows
• Columns include:
  • Categorical: Ship Mode, Segment, Country, City, State, Region, Category, Sub-Category
  • Numerical: Sales, Quantity, Discount, Profit

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⚙️ Environment Setup

1. Clone repository

   git clone https://github.com/IngMosri/Python-Data-Science-ML.git
   cd Python-Data-Science-ML
   
    2.	Create and activate virtual environment
   

python -m venv .venv source .venv/bin/activate # macOS/Linux .venv\Scripts\activate # Windows

3.	Install dependencies

pip install -r requirements.txt

4.	Run environment test

python notebook_py/test_env.py

Output confirms Python path and versions of Pandas/Numpy.

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🚀 How to Run the Analysis

From the project root:

python notebook_py/eda_supermarket.py

•	Generates multiple plots (each in a separate figure).
•	All plots are automatically exported to the figs/ folder.

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📈 Example Outputs

Sales Distribution

Top 15 Cities by Sales

Discount vs Profit

Correlation Matrix

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✅ Key Findings (Week 1) • No missing values in the dataset; data types are clean. • Technology is the leading category in sales. • New York City and Los Angeles dominate sales volume. • High discounts strongly reduce profit (negative correlation).

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📌 Progress Log • ✔️ Repo cleaned and reset (removed heavy files, added .gitignore). • ✔️ Configured .venv for Python 3.12. • ✔️ Added base dependencies: numpy, pandas, matplotlib, seaborn. • ✔️ Created requirements.txt for reproducibility. • ✔️ Tested environment with test_env.py. • ✔️ Implemented eda_supermarket.py with Markdown-style sections and clear plots. • ✔️ Added figs/ folder for chart exports. • ✔️ Updated README with structure, setup instructions, outputs, and findings.

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🎯 Next Steps • Week 2: Advanced data manipulation with groupby, pivot_table, and merges. • Improve visualizations (boxplots, pairplots, category breakdowns). • Document insights in README + prepare for first ML task.