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.
A repository focusing on implementing Market Basket Analysis using the Apriori Algorithm in Python, providing insights into customer purchasing behaviour.
Javascript library for Marketing forms in Customer Insights
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
Uncover insights, trends, and patterns within the retail data. Harness the power of data analytics to optimize inventory management, understand customer preferences, and drive strategic decision-making
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.
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.
This component allows you to create a Dynamics 365 solution to set up a custom channel in Customer Insights - Journeys, as an XrmToolbox plugin.
Analyze sales trends and customer behavior for Maven Roasters using Python, Matplotlib, and Streamlit. Key insights optimize operations, boost revenue, and enhance customer satisfaction.
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.
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