MOSUM procedure for multiple change point estimation

Methods for data segmentation under a sparse regression model

This project analyzes the Brazilian e-commerce public dataset by Olist, focusing on customer behavior, satisfaction, and value prediction. The dataset contains information about 100,000 orders made between 2016 and 2018 across multiple marketplaces in Brazil, providing comprehensive insights into various aspects of e-commerce operations.

Joint smoothing and partitioning of one-dimensional signals and time series with higher order Mumford-Shah models

E-commerce sales project

🍎🔍 Smart AI system that identifies food items in photos and calculates their calorie content automatically. Built with TensorFlow, YOLOv8, CUDA and computer vision for accurate nutrition tracking.

Change-point detection, rate-monitoring and pattern analysis for time-tagged event data using Bayesian Blocks (Scargle, 2013) and Sparse Non-Negative Tucker Decomposition (SNNTD)

Unsupervised Machine Learning for Customer Market Segmentation with Python

this project included data preprocessing, feature selection, and K-means clustering to categorize customers

End-to-end SQL analytics project on Amazon India sales data — pricing strategy, discount behavior, customer segmentation, cumulative analytics, and part-to-whole market share analysis using T-SQL and window functions.

Data Analytics Project: Analyzed Promotions and Provided Tangible Insights to Sales Director

This repository includes a curated set of SQL scripts that demonstrate a range of analytical techniques, including time-based trend analysis, cumulative metrics, performance evaluation, data segmentation, and part-to-whole analysis.

This repository contains a comprehensive collection of SQL scripts based on a learning project aimed at practicing data exploration, analytics, and reporting techniques using SQL.

A robust C library for efficient segmentation and reassembly of large JSON objects in IoT and resource-constrained environments. Ensures data integrity and efficiency in network communication.

This project is focused on segmenting e-commerce customers using unsupervised machine learning models, specifically clustering algorithms.

E-commerce exploratory analysis with RFM customer segmentation and metrics

For this project we will attempt to use K-Means Clustering to cluster Universities into to two groups, Private and Public.. The algorithm uses unsupervised learning.

Solution to Data@ANZ Virtual Experience Program with Forage.

A manaual Data Augmention for images data also there's an automated approcah by Augmentor libraray

Customer segmentation using clustering techniques on retail data.