标签：#analyst

Introduction In this project, I set out to analyze a dataset of products listed on Jumia, one of Africa's leading e-commerce platforms, with the goal of understanding how pricing strategies, discounts, and customer reviews influence overall product performance. Using Microsoft Excel, I cleaned and prepared the raw dataset, engineered new features to categorize products, ran correlation and trend analyses to uncover relationships in the data, and ultimately built an interactive dashboard to present the findings in a clear, business friendly format. The objective was not just to explore the numbers, but to answer practical business questions: Do bigger discounts actually drive more customer engagement? Does price influence how a product is rated? Which products are genuinely performing well, and which ones might need a different pricing or marketing approach? This article breaks down the full process behind how I turned raw data into a finished report from initial data cleaning to the creation of an interactive dashboard. The following is the uncleaned data(original dataset) Data Set Overview. The dataset contains information about products listed on Jumia with the following columns: • Product: Name of the product. • Current Price: The current selling price of the product (in KSh). • Old Price: The original price before discount (in KSh). • Discount: The percentage discount offered on the product. • Review: The number of customer reviews received by the product. • Rating: The average customer rating of the product (out of 5). Data Cleaning and Preparation On a separate sheet after copy pasting the original data set I had to ensure there was no any present duplicate in the dataset. Now that I had removed any duplicates that was present, next process was changing the cell format for each column from General to the appropriate data type i.e. Text for product names, Currency for prices, Percentage for Discount and Number for ratings and reviews. Next, I used Find and Repl

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