This project is a complete end-to-end retail sales analysis built using Python, Pandas, and visualization libraries. It explores customer behavior, monthly trends, product category performance, and key revenue drivers using a real retail dataset.

Cleaned and processed real-world retail sales dataset

Missing value handling & proper date-time formatting

Added new features: Month, Year, Weekday

Exploratory Data Analysis (EDA)

Category-wise performance insights

Gender-based revenue analysis

Sales trend visualizations

Cleaned dataset included for reuse

Python

Pandas

NumPy

Matplotlib

Seaborn

Google Colab

📦 Retail-Sales-Analysis/ │ ├── data/ │ ├── retail-sales-dataset.zip # Raw dataset │ ├── cleaned_retail_sales.csv # Processed dataset │ ├── notebooks/ │ └── retail_sales_analysis.ipynb # Full analysis notebook │ ├── images/ │ ├── category_sales.png │ ├── monthly_trend.png │ ├── weekday_sales.png │ ├── gender_sales.png │ └── README.md

git clone https://github.com/its-Adarsh2003/Retail-Sales-Analysis.git

Install Dependencies pip install pandas numpy matplotlib seaborn

Open the Notebook

Use either:Google Colab/Jupyter Notebook

Open:notebooks/retail_sales_analysis.ipynb

Run All Cells

The notebook will:

Load the raw dataset

Clean & process it

Generate all charts

Save the cleaned CSV

📌 Key Insights

Electronics & Clothing generated the highest revenue.

December & November show strong seasonal peaks.

Saturday has the most sales activity.

Female customers contributed slightly more revenue.

Sales dominated by the 25–40 age group.

📬 Contact If you’d like help with Data Analytics, EDA reports, dashboards, or ML projects, feel free to reach out!

LinkedIn: www.linkedin.com/in/adarsh-dubey-81881a2a5 Email: dubeyadarsh138@gmail.com

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