1.
Project Introduction and Objective In this project, I used Microsoft Excel and Power Query to clean and analyze a Jumia product dataset and then built an interactive dashboard to summarize pricing, discounts, ratings and customer engagement.
The main objective was to turn a small raw e-commerce dataset into useful business information.
I wanted the final dashboard to answer practical questions such as: Do products with higher discounts receive more customer engagement?
Do higher priced products have better ratings?
Is there a relationship between product rating and number of reviews?
Which products have the highest review engagement?
Which products may require further investigation because they have high discounts but low ratings?
The project also gave me practical experience in data cleaning, excel formulas, PivotTables, PivotCharts, slicers, correlation analysis and dashboard design.
2.
Dataset and Business Questions The original dataset contained 115 rows and 6 columns: Product Current price Old price Discount Review Rating The dataset was small but it contained several realistic data quality problems.
This made it useful for me to practice the complete analytics process rather than going directly to visualization.
I structured the workbook into the following sheets: As we have always been taught in class,I kept the sheet unchanged so that I always have a copy of the original source data.
3.
Initial Data-Quality Audit Before cleaning the data, I profiled the dataset in Power Query using Column Quality, Column Distribution and Column Profile.
The audit identified several issues: Data-quality check Result Original rows 115 Original columns 6 Blank Review values 58 Blank Rating values 58 Populated Review values stored as negative numbers 57 Current Price ranges 1 Old Price ranges 1 Exact duplicate rows removed 3 Discount values outside 0 to 100% 0 Rating values outside 0 to 5 after cleaning 0 One unusual issue was the Review field.
All populated review counts were negative, even though a review count cannot logically be negative.
I therefore treated the negative sign as a data collection or scraping issue rather than a genuine business value.
The Rating field also required cleaning because values were stored as text such as: rather than as numeric ratings.
This audit was important because it prevented incorrect calculations later in the project.
4.
Cleaning and Preparation Decisions I performed the main cleaning steps in Power Query.
Product names I applied Trim and Clean to remove unnecessary spaces and non-printable characters.
Repeated product names were not automatically deleted because two rows with the same product name are not necessarily duplicates.
Current and old prices The price fields contained KSh and commas so I removed the currency text and converted the fields to numeric values.
I notieced that two records contained price ranges rather than single prices: Current Price: Old Price: I used the midpoint of each range: Current Price midpoint = KSh 1,800 Old Price midpoint = KSh 2,700 This decision was documented in the Data Dictionary.
Discount The Discount field was already interpreted correctly as a percentage.
The cleaned values ranged from 1% to 64%, so I did not divide the field by 100 or remove its percentage data type.
Reviews The populated Review values were negative.
I used Absolute Value in Power Query to convert them to valid positive counts.
The missing reviews remained blank.
I deliberately did not convert missing reviews to zero because a blank means that the review information is unavailable, while zero would mean that the product definitely had no reviews.
Ratings I renamed to , removed the text and converted the result to a decimal number.
The final valid ratings ranged from 2.0 to 5.0.
Duplicate records I checked duplicates using the analytical fields: Product Current Price Old Price Discount Review Rating Three redundant duplicate rows were removed, reducing the dataset from 115 to 112 product