US Regional Sales Data

This dataset provides comprehensive insights into US regional sales data across different sales channels, including In-Store, Online, Distributor, and Wholesale. With a total of 17,992 rows and 15 columns, this dataset encompasses a wide range of information, from order and product details to sales performance metrics. It offers a comprehensive overview of sales transactions and customer interactions, enabling deep analysis of sales patterns, trends, and potential opportunities.

Columns in the dataset:

• OrderNumber: A unique identifier for each order.
• Sales Channel: The channel through which the sale was made (In-Store, Online, Distributor, Wholesale).
• WarehouseCode: Code representing the warehouse involved in the order.
• ProcuredDate: Date when the products were procured.
• OrderDate: Date when the order was placed.
• ShipDate: Date when the order was shipped.
• DeliveryDate: Date when the order was delivered.
• SalesTeamID: Identifier for the sales team involved.
• CustomerID: Identifier for the customer.
• StoreID: Identifier for the store.
• ProductID: Identifier for the product.
• Order Quantity: Quantity of products ordered.
• Discount Applied: Applied discount for the order.
• Unit Cost: Cost of a single unit of the product.
• Unit Price: Price at which the product was sold.

This dataset serves as a valuable resource for analysing sales trends, identifying popular products, assessing the performance of different sales channels, and optimising pricing strategies for different regions.

Visualization Ideas:

• Time Series Analysis: Plot sales trends over time to identify seasonal patterns and changes in demand.
• Sales Channel Comparison: Compare sales performance across different channels using bar charts or line graphs.
• Product Analysis: Visualise the distribution of sales across different products using pie charts or bar plots.
• Discount Analysis: Analyse the impact of discounts on sales using scatter plots or line graphs.
• Regional Performance: Create maps to visualise sales performance across different regions.

Data Modelling and Machine Learning Ideas (Price Prediction):

• Linear Regression: Build a linear regression model to predict the unit price based on features such as order quantity, discount applied, and unit cost.
• Random Forest Regression: Use a random forest regression model to predict the price, taking into account multiple features and their interactions.
• Neural Networks: Train a neural network to predict unit price using deep learning techniques, which can capture complex relationships in the data.
• Feature Importance Analysis: Identify the most influential features affecting price prediction using techniques like feature importance scores from tree-based models.
• Time Series Forecasting: Develop a time series forecasting model to predict future prices based on historical sales data.
• These visualisation and modelling ideas can help you gain valuable insights from the sales data and create predictive models to optimise pricing strategies and improve sales performance.