Crandi

Github Lokesh Analyst Ecommerce Sales Analysis A Data Driven Project

Leo Migdal
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github lokesh analyst ecommerce sales analysis a data driven project

This project analyzes an e-commerce dataset to uncover key business insights and provide actionable recommendations. The analysis follows the data analytics process: Ask → Prepare → Process → Analyze → Share → Act. Here are some example charts generated in the analysis: Create an advanced data engineering pipeline that processes and analyzes sales data from an e-commerce website using Apache Airflow for workflow management and ClickHouse as the high-performance data warehouse. This repository is created by Dharshan Kumar K S and Siva Prakash as part of our semester project from 'Big Data Analysis' subject In this project we dive into the intriguing world of Ecommerce sales data from the year 2019.

Through data wrangling, visualization, and insightful analysis, we aim to uncover trends, customer behaviors, and key factors that drove sales during that period. ETL pipeline and data warehouse for e-commerce analytics using PostgreSQL and Python. Includes transformation scripts, schema modeling, and business insights via SQL. University project provided by Alkemy. Market analysis and strategic consultancy for a possible client in the retail sector. Run the analysis in Jupyter Notebook to execute the SQL queries and visualize data insights.

This end-to-end data analytics project analyzes four years of e-commerce sales data to uncover meaningful business insights such as sales trends, customer behavior, product performance, and regional growth. This project showcases the workflow of a Data Analyst handling raw data all the way to building dashboards for decision-making. ✔ Identify top-performing states, cities, customers, categories ✔ Analyze year-wise and month-wise sales trends ✔ Calculate profit distribution across regions & categories This project focuses on analyzing and managing data for an e-commerce platform.

The objective is to build insights into customer behavior, product performance, and operational efficiency. The project uses SQL for data handling, Python (via Jupyter Notebook) for ETL processes, and various datasets to generate actionable insights. The ETL (Extract, Transform, Load) pipeline is a critical part of this project. It processes raw e-commerce data into clean, structured formats, ready for analysis. The ETL pipeline is implemented in Python within the ETL_ECommerce_Project.ipynb notebook. This ETL process is designed for flexibility and adaptability, making it a robust framework for handling e-commerce data workflows!

This project analyzes customer segmentation and behavior using data science and cohort analysis. Key metrics like CRR, NRR, CLR, and CLV are examined through detailed charts, including the cohort layer cake and CLR vs. CLV cost efficiency analysis. Exploratory Data Analysis and systematic data manipulation reveal actionable insights. Analyzed 49K+ sales records using Pandas & Matplotlib. Identified top products and customer trends with visual insights.

The Coffee Orders and Sales Dashboard uses Excel to visualize key data, including sales by country, top customers, roast type, and order date, helping optimize operations and boost sales through data-driven insights. This project involves analyzing a sizable sales dataset to identify patterns, best-selling items, and key revenue indicators. The goal is to provide data-driven insights to support business decision-making and improve sales strategies. This project involves analyzing sales data for a company to gain insights into sales trends, customer behavior, and business performance. The dataset used for this project contains information about orders, customers, products, and sales transactions. This project involves analyzing e-commerce data using SQL, Excel, and Power BI to derive insights and visualize key metrics.

The main objectives are to identify trends, track sales performance, and provide a comprehensive overview of business operations. This dashboard provides a comprehensive analysis of sales and net profit. This dashboard focuses on a more detailed analysis of net profit. This project showcases the use of SQL, Excel, and Power BI to analyze and visualize e-commerce data effectively. The insights derived can help in making informed business decisions. Extracted orders data for an Ecommerce business using the Kaggle API into a Pandas dataframe.

Performed data cleaning and transformation in Python using various string and datetime functions available within Pandas. Loaded the cleaned dataset to a MySQL database. Used SQL to draw insights on highest revenue generating products, highest selling products in each region, growth by profit among product categories in 2023 as compared to 2022 and month over month growth comparison... Used advanced SQL to solve real world business problems for a product based company such as payment funnel analysis, calculating MoM revenue for the company, flagging upsell oppurtunities and tracking subscription cancellation reasons among... Assisting "PhoneNow", a telecom client with BI tasks such as presenting call centre trends and performing customer churn analysis. Manipulated data of a ficticious bank in Power BI to relate customer churn rates across various age groups, tenure, region and card types in order to drive future strategies for customer retention.

Pulled sales data of a fictional E-commerce store and presented key metrics such as the average order value, monthly profits, revenue by region, top customers and the proportion of different payment methods used for... in a Power BI dashboard.

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