We work on generating insights from sales data, finding patterns of orders, Discovery any clear trends or causality. This report provides insights about the top-performing and underperforming Store and delivery, find a solution to the problem of high variation time waiting until the order is picked-up A- Finding any clear trends / causality? • What other causes that are not in the given data table? • can you think of affecting the number of times an order is reassigned? • Based on your finding propose at least one action to tackle this issue. B- Once the courier arrives to the store, • the time until the order is picked-up can present very high variation. • With the data given, present an analysis showing this, giving as much as detail as possible. • Can you identify any clear trends in terms of worst offenders? • What could be causing this high variability? • What could be the possible causes? • when is the courier waiting for a very long time before picking up the order? • Propose at least one action to Code Link of code: https://colab.research.google.com/drive/1s0jslFy8e4K085XaISe9M4eKr54aZX5c?usp=sharin g Dependences The main dependencies for the project are Tableau, Excel, and Python data analysis libraries. Guiding questions What tools are you choosing and why? • I'm using Tableau, Excel tools Because of the small dataset. And Python for this effective cleaning process of data. Have you ensured your data’s integrity? Yes, the data is consistent throughout the columns. What steps I have taken to ensure that your data is clean? • First the missing values where removed, then the columns formatted to their correct format. How can you verify that your data is clean and ready to analyze? • Yes, it's all documented in this python notebook. • Document the cleaning process.