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BouchentoufOthman / repository
Retail sales analysis project using Python (Jupyter Notebook) for data cleaning, outlier detection, feature engineering, and RFM-based customer segmentation, followed by an interactive Power BI dashboard for visual insights on sales trends, customer segment, and regional performance.
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This project analyzes retail sales data using Python for data preprocessing and customer segmentation, and visualizes key insights in a Power BI dashboard. It combines data science with business intelligence to support stakeholder decision-making.
This project consists of two main parts:
The dataset is based on an online retail transaction dataset, containing sales records such as:
EIRE → Ireland).UnitPrice and extreme outliers.IsRefund.TotalPrice = Quantity × UnitPrice.UnitPrice (due to skewed distribution).Quantity (due to more normal distribution).Selected from shared topics, language and repository description—not editorial ratings.
parkerglas12 /
Analyzing sales data to uncover insights into purchasing behavior and other business-related variables. Written in Jupyter Notebooks using Pandas, Matplotlib, and Seaborn.
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50/100 healthdenomand123 /
We created an RFM table per customer:
Recency: Days since the customer's last purchaseFrequency: Total number of purchasesMonetary: Total money spentTotalPrice: Total price of a set of products boughtRefund transactions were excluded from this calculation.
log1p transformation to reduce skew in RFM values.StandardScaler.| Cluster | Recency | Frequency | Monetary | Interpretation |
|---|---|---|---|---|
| 3 | 17.4 | 2.2 | 563.5 | 🟠 New Customers |
| 1 | 12.2 | 13.7 | 7662.5 | 🟢 Champions |
| 2 | 73.1 | 4.0 | 1622.8 | 🔵 Loyal Customers |
| 0 | 182.8 | 1.3 | 328.5 | 🔴 At-Risk Customers |
Segment labels were assigned and merged back into the retail dataset (non-refund only).
You can explore the interactive Power BI dashboard for this project here:
Retail Sales & Customer Segmentation Dashboard
📋 BI Dashboard
The final dashboard includes the following visuals:
TotalPrice by regionThe datasets used in this project include:
This project leveraged the following tools:
pandas, numpy, matplotlib, seaborn, and scikit-learn.This repository contains a data analysis project focused on Amazon retail sales data. It includes a Jupyter Notebook with analysis and insights, as well as the dataset for exploring product prices, ratings, and user reviews.
A focused, business-driven sales analysis pipeline on invoice-level data (provided as `sales.xlsx`) delivering actionable insights: data cleaning, EDA, market performance, RFM customer segmentation, and customer retention analysis — all implemented in a production-ready Jupyter notebook.
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Exploratory Data Analysis (EDA) on Online Retail sales data using Python and Jupyter Notebook. The project focuses on data cleaning, sales trend analysis, product and country performance, customer behavior insights, and anomaly detection. Designed as a foundation for Power BI dashboarding and business intelligence reporting.
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