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Github Runalimayanache Ecommerce Sales Analysis This Project

Leo Migdal
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github runalimayanache ecommerce sales analysis this project

[E-Commerce Sales Analysis Project]- This project analyzes an e-commerce dataset using Excel for data cleaning and Power BI for building an interactive dashboard. The goal is to uncover key business insights that help understand customer behavior, revenue patterns, and overall business performance. [Project Highlights] ✔️ Data cleaning and preprocessing in Excel ✔️ Interactive dashboard built in Power BI ✔️ Insights on: Monthly sales trends Top-performing cities Gender-wise order distribution Order status breakdown ✔️ Business-focused storytelling with clean visuals [Tools Used] Microsoft Excel – Cleaning, formatting, preprocessing Power BI – Dashboard design, DAX basics, business insights [Key Insights] -Peak sales observed during festive months -Customer orders higher in major metro cities -Female customers contributed slightly more to total orders -Most orders fall under Delivered and Shipped categories

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. 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: Using Python Pandas Library to Analyze Sales Data

A service sales manager made for freelancers. A visually interactive AI-powered sales forecasting dashboard using Streamlit, Plotly, and Python. Visualize KPIs, trends, and export filtered data with ease. Enable stakeholders with an engaging Power BI dashboard illustrating key e-commerce performance indicators and patterns. Evaluate sales data, predict forthcoming trends, and formulate knowledgeable approaches to foster business expansion. This project predicts the sales demand for various items in different stores based on historical sales data.

The objective is to develop a machine learning model that can provide accurate forecasts for future sales of each store-item combination. Run the analysis in Jupyter Notebook to execute the SQL queries and visualize data insights. This data analysis project aims to provide insights into sales performance of an e-commerce company over the past year. By analyzing various aspects of the sales data, we see to identify trends, make data-driven recommendations, and gain a deeper understanding of the company's performance. Sales Data: The primary dataset used for this analysis is the "Amazon Sales Dataset(raw data).xlsx" file, containing detail information about each sales made by the company. In the initial data preparation phase, we performed the following tasks:

EDA involved exploring the sales data to answer key questions, such as: The analysis results are summarized as follows: This Python program analyzes e-commerce sales data by performing data exploration, cleaning, feature engineering, visualization, and machine learning techniques. It provides insights on the relationship between sales and quantity, unit price, country, and offers recommendations to improve sales. Load the dataset into a Pandas DataFrame Perform initial data exploration using head(), shape, and dtypes functions Check for missing values and handle them appropriately Convert non-numeric values in Quantity and UnitPrice columns to... We can use linear regression, decision trees, or other models depending on the data and problem statement.

Summarize findings and provide recommendations based on the analysis. We can suggest ways to improve sales, identify areas of improvement, and highlight key factors that influence sales. A Jupyter notebook containing Python code for data preprocessing, exploratory data analysis, and sales prediction Data visualization using Seaborn plots and correlation heatmap Model selection, training, and evaluation for sales prediction A report summarizing... Python and Pandas for data preprocessing and exploratory data analysis Seaborn and Matplotlib for data visualization Scikit-learn for machine learning and sales prediction Jupyter notebook for code development and documentation. 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. This project analyzes an online retail dataset to uncover sales trends, top products, best customers, and other actionable business insights. It demonstrates key data science skills:

This data analysis aims to provide insights into sales performance of an e-commerce company over the past years. By analysing various aspects of the sales data set, i seek to identify trends, make data driven recommendations, and gain a deeper understanding of the company’s performance. Sales Data: Data Source The data used for this analysis was queries written on a Relational Database Management System(MySQL) to return the necessary data containing detailed information about each sales made by the company. EDA involve exploring the HR data to answer key questions, such as: The analysis results are summarized as follows: Based on the analysis, i recommend the following actions:

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