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Food Delivery

INSTACART

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Analyzed data to find customer preferences and sales patterns by using Python 3 to clean, integrate, and visualize data provided by Instacart.

Data

Fictional customer base

https://d.docs.live.net/acfb05d6f4fa6932/Documents/Master%20folder/Project%20Management/Data

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Analytical Techniques

  • Data Wrangling

  • Cleaning with Python

  • Data merging Deriving variables

  • Grouping and aggregating data

  • Manipulating data

  • Frames Data

  • visualization with matplotlib and Seabor

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  • Exploring basic patterns such as busiest days, days with most orders or busiest hours of the day

  • Used Python commands to wrangle and clean data

  • Connected data sets

  • Performed commands to group, aggregate and summarize

  •  Derived insights and used Python to create visualizations

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  • Customer profiles were divided by age, loyalty,  family size, and marital status.

  • They were further grouped and grouped by regions

  • This provided insights into customer’s spending habits based on different socioeconomic backgrounds

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Results

What we can see is that the regular customers mostly consist of the middle-aged people who are buying lower priced items more frequently. They are order produce, milk eggs, snacks, beverages, and frozen items most often. We can also see a large number of our regular customers live in the Southern states.

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​Recommendations

  • What we can do to increase other regions to have a higher regular customer population is to advertise more. For example, the West is close behind with regular customer numbers, then Midwest, and Northeastern regions.

  • Reflect and see if advertising is higher in the Southern regions than the Northeast. Advertising more or if it is a delivery issue, promote to delivers more.

  • To try to increase the Regular customer number we can take the Loyal customers and give them incentives to become Regular customers. Incentives could be anything from a rewards program where they purchase a certain dollar amount in a month, and they get a certain dollar amount off their next order.

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