Retail Sales Analyzer

This Python script provides a simple and effective way to analyze retail sales data using the Pandas and Matplotlib libraries. The script reads sales data from a CSV file, processes it, and provides various insights and visualizations.

Features

• Data Cleaning: The script removes any missing values from the dataset.
• Total Sales Per Product: Calculates the total sales for each product.
• Best Selling Product: Identifies the best-selling product based on total sales.
• Average Daily Sales: Computes the average daily sales across the dataset.
• Sales Trend Visualization: Plots the sales trend over time.
• Sales Per Product Visualization: Plots the total sales per product in a bar chart.

Requirements

• Python 3.x
• Pandas
• Matplotlib

You can install the required packages using pip:

pip install pandas matplotlib

Usage

1. Data Preparation: Ensure you have a CSV file named retail_sales.csv with the following columns:

   • Date: The date of the sales (in YYYY-MM-DD format).
   • Product: The name of the product.
   • Sales: The sales amount.

2. Running the Script: Simply run the script to get the analysis results. The script will print out the total sales per product, the best-selling product, and the average daily sales. It will also display two plots: one for the sales trend over time and another for sales per product.

analyzer = RetailSalesAnalyzer()
print('Total Sales per Product: \n', analyzer.total_sales_per_product())
print('Best Selling Product: ', analyzer.best_selling_product())
print('Average Daily Sales: ', analyzer.average_daily_Sales())
analyzer.plot_sales_per_product()
analyzer.plot_sales_trend()

Outcomes

Textual Output:

• Total Sales per Product: Shows the total sales for each product.
• Best-Selling Product: Identifies the product with the highest sales.
• Average Daily Sales: Displays the average daily sales across all dates.

Visual Output:

• Sales Per Product Bar Chart: Visualizes the sales distribution among products.
• Sales Trend Line Chart: Plots the sales trend over time.

Conclusion

This script provides a comprehensive way to analyze retail sales data, from cleaning and processing the data to performing key analyses and visualizations. The class-based structure makes