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.

Analyse the customer purchase behaviour to optimize inventory cost

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

This project is based on supply chain analytics along with demand forecasting and inventory management of the top selling product. Demand forecasting is done by using the prophet time series model. Also, the dashboard consists of all the important insights related to customers, products, orders as well as the forecasting outcomes.

Solution to Quantium Virtual Internships on Forage

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.

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.

The Big Mart Sales Prediction project aims to predict the sales of various products at different Big Mart stores using machine learning algorithms

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

Leveraging K-Means clustering, our project categorizes retail customers based on purchasing behaviors and demographics. This provides businesses with actionable insights to tailor marketing efforts, enhancing customer experience and boosting sales.

Recommendation system using ML

This project analyzes data , focusing on key metrics such as total sales, profit, year-over-year growth, and regional performance. The dashboard visualizes insights related to help identify sales drivers and opportunities for optimization. Recommendations are provided based on the analysis to enhance sales strategies and improve profitability.

Implementation of Exploratory Data Analysis on Supermarket Sales Data with MySQL Workbench

Retail Analytics in Shopping Malls

Leveraging K-Means clustering, our project categorizes retail customers based on purchasing behaviors and demographics. This provides businesses with actionable insights to tailor marketing efforts, enhancing customer experience and boosting sales.