Erick Kiprotich YEGON
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Competition - Meal Delivery Service
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    How can a meal delivery service improve operations?

    📖 Background

    Your client is a food company in India that provides meal delivery services to thousands of customers per week. They operate in 51 cities inside eight different regions of the country. To better serve their customers, they deliver meals from 77 fulfillment centers.

    The company wants you to help them understand their customer base, meal preferences, and fluctuations in demand. They want to investigate ways to improve operations and reduce food waste.

    💾 The data

    Your client has provided you with almost three years of order information for each of their 77 fulfillment centers:

    • "week" - week (1 - 145)
    • "center_id" - id of the fulfillment center
    • "city_code" - city identifier
    • "region_code" - region identifier
    • "center_type" - there are three types of centers: A, B, and C
    • "op_area" - the size of the area serviced by the fulfillment center
    • "meal_id" - each meal has a unique id
    • "category" - food category (beverages, pasta, rice, etc.)
    • "cuisine" - the type of cuisine for each meal
    • "checkout_price" - the price the customer pays (including discounts, promotions, etc.)
    • "base_price" - list price for the meal
    • "emailer" - 1 if there was an email promotion for the meal, 0 otherwise
    • "featured" - 1 if the website featured the meal, 0 otherwise
    • "orders" - number of orders for that week for that meal id
    suppressPackageStartupMessages(library(tidyverse))
deliveries <- readr::read_csv('data/meal_deliveries.csv', show_col_types = FALSE)
deliveries

    💪 Competition challenge

    Create a report that covers the following:

    1. What are the most popular food categories in each region?
    2. For each of the two cities with more than three fulfillment centers, would you recommend:
      • Keeping the number of centers the same,
      • Combine fulfillment centers, or
      • Opening new centers
    3. Investigate the effectiveness of email campaigns and promoting meals on the website.
    4. Explore ways of forecasting food order numbers to assist purchasing managers in their planning.

    🧑‍⚖️ Judging criteria

    CATEGORYWEIGHTINGDETAILS
    Recommendations35%
    • Clarity of recommendations - how clear and well presented the recommendation is.
    • Quality of recommendations - are appropriate analytical techniques used & are the conclusions valid?
    • Number of relevant insights found for the target audience.
    Storytelling35%
    • How well the data and insights are connected to the recommendation.
    • How the narrative and whole report connects together.
    • Balancing making the report in-depth enough but also concise.
    Visualizations30%
    • Appropriateness of visualization used.
    • Clarity of insight from visualization.

    ✅ Checklist before publishing into the competition

    • Rename your workspace to make it descriptive of your work. N.B. you should leave the notebook name as notebook.ipynb.
    • Remove redundant cells like the judging criteria, so the workbook is focused on your story.
    • Make sure the workbook reads well and explains how you found your insights.
    • Try to include an executive summary of your recommendations at the beginning.
    • Check that all the cells run without error

    ⌛️ Time is ticking. Good luck!

    # install.packages("tidyquant")
library(tidyverse)
library(tidymodels)
library(tidyquant)

    EDA

    #lets check if there are any missing values in each feature
deliveries |> 
    sapply(function(x) sum(is.na(x)))

    1. What are the most popular food categories in each region?

    food_category_by_region_tbl <- deliveries |> 
    select(region_code, category)

food_category_by_region_tbl |> 
    count(region_code, category) |> 
    arrange(desc(n))

food_category_by_region_tbl |> 
    mutate(n=n(),
           category= reorder(category,n)) |> 
    ggplot(aes(x=region_code, y= category))+
    geom_col(aes(fill = category))+
    scale_x_continuous(labels = scales::label_number())+
    #scale_x_continuous(labels = scales::dollar_format(scale = 1e-6, suffix = "M"))+
    facet_wrap(~ region_code,scales = "free_x")+
    guides(fill = "none")  +
        labs(
        title = "Popular Food Categories by Region",
        subtitle = "Regions 77, 56 and 34 are the main regions with food distributions by category",
        caption = "Across all the regions, Beverages, Sandwich, Rice bowl, Pizzas and Starters respectively\n are the main categories of foods ",
        y = "Food Category",
        x = " "        )+
       scale_color_tq()+
    theme_tq()