Data Mining project 2020/2021 @ University of Pisa
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Updated
Mar 9, 2021 - Jupyter Notebook
Data Mining project 2020/2021 @ University of Pisa
RFM (Recency, Frequency, Monetary) Analysis on an Online Retail Customers using K-Means Clustering with Python
Online retail store website project.
This Cohort table sorts customers by when they made their first purchase and calculate the retention rate of each groups by checking how many of them keep buying over months.
ECOMMAPP is a robust and user-friendly eCommerce platform designed to provide a seamless online shopping experience. The application offers a wide range of products, easy navigation, and secure payment options, catering to both customers and merchants.
Mini Portfolio Analisis Penjualan pada sebuah Ritel Online
Analyze product data for an online sports retail company to optimize revenue by using SQL
The 3DToyBox E-commerce store is a modern online platform designed to cater to the growing demand for customizable collectibles. With an intuitive user interface and robust backend infrastructure, the website offers customers a seamless shopping experience.
Online Retail Store DBMS made using MySQL and Flask
RFM model-based Customer Segmentation using Clustering, Classification and BTYD Models
PySpark data analysis of the Online Retail Data Set
This project focuses on exploring product recommendations within the realm of luxury fashion resale. It serves as a companion to my thesis of the MSc Marketing Analytics program at TiU.
An online retail store end to end database application
code for kaggle OTTO competition (got silver medal)
This is a code sample repository for online retail product recommendations using Collaborative Filtering (Memory-Based, aka History-Based). The source data used the famous Online Retail Data Set from UCI Machine Learning Repository.
Online Retail Dashboard
Oat : Hi !
Online Retail Database Project is a example of a design and database for an online shop that sells various item.
Analytical SQL Project where we had to analyze the dataset and come up with 5+ insights and divide the customers into segments using analytical functions.
Dive into comprehensive e-commerce sales analytics with this Excel-based dashboard. Gain valuable insights into key metrics, visualize trends, and make informed business decisions. Explore the GitHub repository for the source code and start optimizing your e-commerce performance.
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