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Portfolio Project: Online Retail Exploratory Data Analysis with Python

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Introduction

As an entry-level data analyst at an online retail company, I embarked on a journey to interpret real-world data and make crucial business decisions. This project involves exploring transactional data, unraveling sales trends, understanding customer behavior, and identifying popular products.

Case Study

In this endeavor, my objective is to conduct exploratory data analysis, unveil patterns, outliers, and correlations, and provide actionable insights. Through visualizations and statistical analysis, I aim to optimize the store's operations and enhance customer satisfaction in the competitive online retail market.

Project Objectives

  • Describe data to answer key questions and uncover insights.
  • Gain valuable insights to improve online retail performance.
  • Provide analytic insights and data-driven recommendations.

Key Findings

  1. Customer Insights:

    • Customer with the highest orders is from the United Kingdom (UK).
    • New customers generate more revenue than existing ones.
  2. Order Statistics:

    • Top 5 countries with the highest orders: UK, Germany, France, Ireland (EIRE), Spain.
    • November 2011 records the highest sales.
    • Approximately 25% NA values present, impacting results.
    • 39.98% of orders are canceled.
    • Top 5 countries with canceled orders: UK, Germany, EIRE, France, USA.
    • Most customers buy less than 25 items.
  3. Sales Timing:

    • Most sales occur in the afternoon, with the lowest sales at night.
  4. Product Insights:

    • Top product is the white hanging heart T-light holder.
  5. Free Items:

    • Unclear why FREE items are given; on average, 2-4 times/month (except June & Dec 2011).

Recommendations

  1. Focus on top markets: UK, Germany, France, Ireland (EIRE), and Spain.
  2. Improve customer retention through loyalty programs or personalized promotions.
  3. Investigate and reduce canceled orders.
  4. Clarify the free item policy for fair treatment.

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Python

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Online Retail Exploratory Data Analysis with Python

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