I obtained generated retail data from Keith Galli, the dataset obtained had about 200,000 orders with varying different aspects about them. From this, i performed feature engineering and data cleaning in extracting date, time data and locations to give me further insights on the best revenue generating months/cities. Under certain assumptions, I was able to model the optimal time to advertise.

Furthermore, i went and extracted key business insights such as the best selling products and the best complements of products bought (i.e. which two products do customers buy together the most?).

Finally: i used Plotly a tool to make integrated visualizations.

1 - Charting the best month for sales based on sales revenue.

2 - The city with the highest sales revenue

3 - The best moment to display advertisements based on time order was placed

• Assumption that customers placed orders right after viewing an advertisement

4 - Which products are the best complements and sell the best together?

5 - Which was the best selling product overall * Charting quantity sold of product to product price and visualizing any relationship

• Data Science