/Quantium-virtual-internship

Quantium Virtual Experience Program hosted by Forage

Primary LanguageJupyter Notebook

Quantium Data Analytics Virtual Experience Program

This virtual experience program focuses on analyzing supermarket chip purchases to understand customer behavior and assess the success of a new store layout trial. The program consists of three tasks:

Task 1: Data Preparation and Customer Analytics

  • Objective: Clean and analyze customer purchase behavior data.
  • Files: quantium_task1.ipynb reads from QVI_purchase_behaviour.csv and QVI_transaction_data.xlsx.
  • Process:
    • Data Cleaning: Converted dates from integer to datetime format, removed salsas and outliers.
    • Analysis:
      • Examined customer segments based on LIFESTAGE and MEMBER_TYPE.
      • Focused on the purchasing habits of the Mainstream Young Singles/Couples segment, analyzing chip brand and packet size preferences.
  • Key Insights:
    • Top three segments by total sales: Budget Older Families, Mainstream Young Singles/Couples, and Mainstream Retirees.
    • Older families purchase more packets on average, while the Mainstream Young Singles/Couples have the largest population.
    • Across most segments, Kettles chips and 175g packets are the most popular.
    • Mainstream Young Singles/Couples are more likely to purchase Tyrells chips and larger 270g packets, particularly Twisties.

Task 2: Experimentation and Uplift Testing

  • Objective: Evaluate the impact of a new store layout on sales and customer numbers.
  • Files: quantium_Task2(1).ipynb reads from QVI_data.csv.
  • Process:
    • Store Matching: Paired trial stores with control stores based on combined metrics using Pearson correlations and magnitude distances.
    • Hypothesis Testing: Assessed whether differences in performance between control and trial stores were statistically significant.
  • Key Insights:
    • Control and trial store pairs identified: 77 with 233, 86 with 155, 88 with 40.
    • Significant sales increases in stores 77 and 86, especially in March and April 2019.
    • Significant customer number increases in stores 77 and 86.
    • No significant performance change in store 88.

Task 3: Analytics and Commercial Application

  • Objective: Summarize findings and provide commercial insights.
  • Output: PowerPoint report using the Pyramid Principle to communicate key insights from Tasks 1 and 2.

Dependencies

  • Language: Python 3.8
  • Packages: pandas, matplotlib, mlxtend, datetime, sklearn, scipy

Project Overview

The goal was to analyze chip purchasing behavior, evaluate the impact of a new store layout, and provide actionable recommendations based on customer preferences and trial performance.