Product Range Analysis

Masterschool's capstone project integrating skills and tools for data analysis.

Project Goal

• Identify the top performers: top customer, top product, and top category
• Identify the customer segments based on RFM
• Did higher-priced products contribute to higher sales than the lower-priced products?
• Compared to other months, were sales higher during the Christmas season (December)?

Project Scope

• Data Preprocessing
• Exploratory Data Analysis
• Customer Segmentation based on RFM Metrics (using percentile ranking and K-means clustering)
• Product Categorization & Product Category Analysis
• Statistical Hypotheses
• Insights
• Dashboard (Tableau)

The project is composed of five Jupyter notebooks:

• 1_data_prep_and_EDA.ipynb
• 2_customer_rfm_segmentation.ipynb
• 3_product_categorization.ipynb
• 4_product_category_analysis.ipynb
• 5_statistical_hypotheses.ipynb

The summary of observations, insights, conclusion, and resources can be found in 5_statistical_hypotheses.ipynb notebook.

Dataset Overview

The dataset is a modified version of the online retail dataset sourced from UCI Machine Learning Repository. It contains 541,909 transaction records from 2018-11-29 to 2019-12-07 which has seven attributes:

• InvoiceNo: Invoice reference number uniquely assigned for each transaction. If the InvoiceNo starts with 'C', it indicates a cancellation
• StockCode: Product or item code uniquely assigned to each distinct product
• Description: Product or item name
• Quantity: The quantities for each product or item per transaction
• InvoiceDate: Invoice date and time, the day and time the transaction was generated by the system
• UnitPrice: Product price per unit
• CustomerID: Customer reference number uniquely assigned to each customer