retail-sales-analysis

Retail sales data analysis using Python and Matplotlib.

This project analyzes retail transaction data using Python (Pandas & Matplotlib) to extract valuable insights into sales trends, customer behavior, product performance, and regional demand. The project includes various visualizations that provide a deeper understanding of key business metrics, which can help identify growth opportunities and areas for improvement in the retail sector.

Dataset

• dataset_matplotlib1.csv is the main data source.
• Columns:
  • order_id: Unique identifier for each order.
  • order_date: Date when the order was placed.
  • customer_id: Unique identifier for each customer.
  • city: The city where the order was placed.
  • province: The province where the order was placed.
  • product_id: Unique identifier for the product sold.
  • brand: The brand of the product sold.
  • quantity: Quantity of the product sold.
  • item_price: Price per unit of the product.

Insights Visualized

The following key insights are visualized through various types of charts:

1. Monthly Sales Trend:

   • This line chart shows the total sales over time, grouped by month. It helps track the overall sales performance and identify trends or fluctuations in sales.

2. Monthly Customer Growth:

   • A line chart showing how many new customers made their first monthly purchase. It provides insights into customer acquisition trends and business growth.

3. Top 5 Provinces by Sales:

   • A bar chart highlighting the top five provinces with the highest total sales. This gives an overview of the most profitable regions.

4. Brand Sales Contribution:

   • A pie chart illustrating the proportion of sales contributed by each brand. This helps understand which brands are driving revenue and their market share.

5. Top 5 Cities by Order Volume:

   • A bar chart showcasing the cities with the highest order volume. This can help identify key customer hubs and regions with high demand.

6. Average Order Value (AOV) by Province:

   • A bar chart comparing the average order value across provinces. This metric helps assess regional revenue efficiency and pricing strategies.

7. Daily Sales Distribution:

   • A line chart that shows how sales fluctuate daily. This helps identify peak sales days and trends in daily performance.

8. Top 10 Best-Selling Products:

   • A bar chart displaying the top 10 products based on total quantity sold. This provides insights into which products are the most popular and contribute the most to sales volume.

Requirements

To run this analysis, you will need the following libraries:

• Pandas: For data manipulation and analysis.
• Matplotlib: For data visualization.

You can install the required libraries using the following commands:

pip install pandas matplotlib

How to Run the Project

1. Clone the repository to your local machine.
2. Ensure the dataset (dataset_matplotlib1.csv) is located in the specified path: C:\FAWAZUL\Bootcamp\DQLab\dataset_matplotlib1.csv. If you're using a different directory or operating system, you must update the file path in the script. For example:
   • On macOS/Linux, the path might look like this: /Users/yourname/Bootcamp/DQLab/dataset_matplotlib1.csv.
   • On Windows, ensure the path includes the correct drive letter and folder structure (e.g., C:\Users\yourname\Documents\dataset_matplotlib1.csv).
3. Run the Python script to generate the visualizations.
4. Analyze the graphs to gain insights into retail sales performance.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

If you'd like to contribute to this project, you can fix the repository, make improvements, and submit a pull request. All contributions are welcome!

Acknowledgments

• The dataset used in this project is provided by DQLab as part of their retail sales analysis exercises.

File Structure

This project contains both the Jupyter Notebook for visualization and the Python script version of the code for cleaner readability and modular use.

• datavis_matplotlib.ipynb: Interactive notebook with code and visualization
• datavis_matplotlib_script.py: Clean script version of the notebook