A repository focusing on implementing Market Basket Analysis using the Apriori Algorithm in Python, providing insights into customer purchasing behaviour.

This project aims to analyze e-commerce data to derive meaningful insights about customer behavior, sales trends, and product performance. We utilize Python, MySQL, and various data visualization libraries to perform the analysis.

Javascript library for Marketing forms in Customer Insights

End-to-End Business Analysis of Flipkart Big Billion Days (BBD) using SQL, Python & Power BI — uncovering sales trends, category performance, customer loyalty, and discount impact.

Analysis and feature engineering of the Online Retail Transactions dataset to uncover customer behaviour, product trends, and optimise pricing. Includes interactive dashboards for actionable insights.

Power BI dashboard analyzing customer insights for an e-commerce business, including sales trends, churn analysis, RFM segmentation, retention, and customer lifetime value.

In this project, I analyzed Blinkit's data using Power BI to uncover key insights and trends. The analysis focuses on customer behavior, operational efficiency, and data-driven decision-making through powerful visualizations and interactive dashboards.

The project aims to estimate the fraction of students who purchased a subscription after starting a lecture, i.e., the free-to-paid conversion rate among students who’ve engaged with video content on the 365 platform.

The Retail Sales Analysis SQL Project uses SQL to explore, clean, and analyze retail sales data. It involves setting up a database, performing Exploratory Data Analysis (EDA), and answering business questions through advanced SQL queries, showcasing skills in data management and insight generation.

This component allows you to create a Dynamics 365 solution to set up a custom channel in Customer Insights - Journeys, as an XrmToolbox plugin.

This Power BI project is a comprehensive analysis and visualization of Adventure Works, and aims to track KPIs, compare regional performance, analyze product-level trends, and identify high-value customers

This project explores sales data to uncover insights about customer behavior and product performance. It helps identify key trends, improve business strategies, and support data-driven decision-making.

Analyze sales trends and customer behavior for Maven Roasters using Python, Matplotlib, and Streamlit. Key insights optimize operations, boost revenue, and enhance customer satisfaction.

Predictive and prescriptive analytics project analysing hotel customer records to identify key satisfaction drivers, build regression models, and generate optimisation strategies for improving guest experience and revenue impact. Includes data pre-processing, exploratory analysis, multi-model comparison, and a prescriptive decision framework.