A modularized SDK library for Amazon Selling Partner API (fully typed in TypeScript)

This repository contains results of the completed tasks for the Quantium Data Analytics Virtual Experience Program by Forage, designed to replicate life in the Retail Analytics and Strategy team at Quantium, using Python.

A Data Analysis project performing Exploratory Data Analysis (EDA) on Global Electronics' data to uncover insights that enhance customer satisfaction, optimize operations, and drive business growth.

DataSpark is a data analysis project using Python, SQL, and Power BI to analyze global electronics retail sales, focusing on customer behavior, sales performance, product profitability, and store performance to optimize sales strategies.

Implementation of a d3.js Visual Analytics dashboard for Sales Analysis and Customer Segmentation in Retail

Analyse the customer purchase behaviour to optimize inventory cost

Analyzed retail sales data using MS SQL Server to uncover trends, optimize performance, and provide actionable business insights.

This project focuses on analyzing sales data from Blinkit, a leading grocery delivery service. Using a real-world dataset, I performed data cleaning in Excel and Power Query, followed by data visualization in Power BI. The result is a dynamic, interactive dashboard that provides KPIs in sales performances across different outlet types & locations.

Data-driven analysis of coffee shop sales using correlation, regression, and causal inference. A Jupyter Book project exploring foot traffic, weather patterns, and business analytics.

Exploring Market Basket Analysis and Using Data Driven Insights to Make store layouts

Solution to Quantium Virtual Internships on Forage

Retail-RAG: A Python-based Retrieval-Augmented Generation (RAG) system for business insights using OpenAI GPT and FAISS. Ingests retail data, generates embeddings, and enables semantic search for financial, customer, and operational insights. Scalable API layer for real-time data-driven decision-making.

This project looks at the sales pattern of a product category in a retail store, using the store’s transaction dataset and identifying customer purchase behavior, to generate insights and recommendations.

Ever wondered what's on a retail shelf just by looking at a photo? Detect and count retail products from both photos and real-time camera feeds—instantly! This smart and simple app uses YOLOv8 to analyze retail shelves, highlight every product it spots, and give you a clear count.

Predicción de ventas en retail utilizando Machine Learning. Comparación de modelos y visualización de insights clave.

The project provides the Apriori algorithm and Market Basket Analysis (MBA) to analyze transactional data, generating personalized recommendations based on Support, Confidence, and Lift metrics to enhance customer experience and boost sales.

Dashboard interactivo de ventas para tienda de vinilos. Análisis visual, KPIs clave y filtros dinámicos para decisiones comerciales.

Visual analytical report of sales data during the Diwali festival period.