OVERVIEW

May 22, 2021 · View on GitHub

The link to the code repository is right HERE

OVERVIEW

This miniature Data Science project was carried out on a supposed Company XYZ which owns a Supermarket chain across Nigeria. Each major branch of the supermarket is located at Abuja, Lagos and PortHarcourt. Sales Data was collected on these branches to help the company understand sales trend, market insights, the market structure up-to-date and how they respond to competitors to stay on top of the industry.

METADATA

The aggregated dataset that was analyzed was combined from three seperate datasets (Market Data from Abuja, Lagos and Port-Harcourt) with each data having the features:

  • Invoice ID: Customer Identification number

  • Branch: Supermarket Branch across the country (A=Lagos Branch, B=Abuja Branch, C=Port Harcourt Branch)

  • City: Supermarket Location

  • Customer Type: Type of customers, Members - Returning customer with membership card, Normal - Customer without membership (could be returning, first-time or walk-in customer)

Gender: Customer Gender Information

  • Product line: Product categorization groups - Electronic accessories, Fashion accessories, Food and beverages, Health and beauty, Home and lifestyle, Sports and travel

  • Unit Price: Price of each product in Naira

  • Quantity: Number of products purchased by customer

  • Tax: 5% tax fee for customer buying

  • Total: Total price including tax

  • Date: Date of purchase (Supermarket Record available from January 2019 to March 2019)

  • Time: Purchase time (Supermarket Hours - 10am to 9pm)

  • Payment: Payment used by customer for purchase (3 methods are available – Cash, Card and Epay)

  • COGS: Cost of goods sold

  • Gross margin percentage

  • Gross income

  • Rating: Customer Satisfaction rating on their overall shopping experience (On a scale of 1 to 10)

WORKFLOWS

The project workflow involved:

  • Getting Descriptive statistics and inferential statistics from our data

  • Performing certain Data aggregation on the supermarket branches,Product line,Gender and many other categorical attributes to gather relevant business insights

  • Generating interactive plots and visualizations for any hidden and extra insights that could help the organization's business decisions