superstore_sales_python_dashboard

The Superstore dataset is a rich and comprehensive dataset containing information on the sales, orders, and customers of a fictional superstore. The dataset provides a detailed view of the business operations of the superstore, including information on the products sold, the customers who purchase them, and the sales performance across different regions and categories. This dataset is a valuable resource for data analysts, business intelligence professionals, and anyone interested in understanding the dynamics of retail sales and customer behavior. With about 9,994 rows of data spanning four years (2014-2017), the Superstore dataset provides a wealth of information to analyze and explore.

In this report, I perform python-based exploratory data analysis to gain insights about the fictional superstore.

This python dashboard provides insights on:

• What is the total sales and profit of the superstore?
• What is the total sales and profit by category?
• Show the top 5 states that made the highest sales
• Show the bottom 5 states that made the highest sales
• What is the total sales by region and category?
• What is the most popular ship mode class?
• Calculate the average profit by region

PROCESS

• Data Cleaning and Preprocessing
  • Before performing analysis, we need to clean and preprocess the data. We will check if there are duplicates and remove any duplicate records and handle any missing values in the dataset.
• Descriptive Statistics
  • To better understand the dataset, we will calculate some descriptive statistics such as mean, median, standard deviation, minimum, and maximum values for the numerical attributes in the dataset.
• Data Visualization
  • To visualize the data, we will create various charts and plots such as bar charts and donut charts. These visualizations will help us to identify patterns and trends in the data.
• Insights/Conclusions
  • Based on the analysis and visualizations, we can draw some insights and conclusions about the Superstore dataset.
• Recommendations
  • Finally, we will provide some recommendations based on the insights/conclusions we have drawn from the analysis. These recommendations will help the superstore to improve its sales and profitability.

INSIGHTS/CONCLUSIONS

• The Total Sales is $2,297,200.86 and the Total Profit is $286,397.02
• Technology has the highest total sales and Furniture has the highest total profit, while Furniture has the next highest total sales and Technology has the next highest total profit. Also, Office supplies made the lowest in both total sales and total profit.
• California made the highest total sales of $457,688, followed by New York that made $310,876 total sales.
• North Dakota has the lowest total sales of $919.91, followed by West Virginia and Maine that made $1209.82 and $1270.53 total sales respectively.
• East Region made the highest total sales in Technology category, and West Region made the highest sales in both Furniture and Office Supplies category. We can also see that the South Region made the lowest sales in all categories.
• Standard Class has the highest ship mode rate of 59.7%, followed by Second Class, first Class and Same Day respectively.
• West Region has the highest average profit.

RECOMMENDATIONS

• Increase focus categories: Based on the analysis, it is clear that some categories are more popular and profitable than others. The superstore should focus on these categories to increase their sales and profitability.
• Improve sales and distribution in underperforming regions: The analysis also reveals that some regions have lower sales and profitability compared to others. The superstore should invest in these regions by improving their sales and distribution strategies to increase their revenue.
• Monitor and optimize discounts: While offering discounts can be an effective way to increase sales, the analysis shows that it can also significantly impact the profitability of the superstore. The superstore should monitor and optimize their discount strategies to ensure that they are not impacting their profitability too much.