Superstore dataset analysis github

Superstore dataset analysis github. The goal of the project is to identify trends and patterns in the data that can help the superstore improve its business performance. To get started with this project, you'll need to have access to Google Colab. Order Date: Date when the order was placed. Perform ‘Exploratory Data Analysis’ on dataset ‘SampleSuperstore’ This notebook is intended for those whose relatively new to EDA (Exploratory Data Analysis) aspect from a Machine Learning process. This dataset pertains to a superstore in the US, providing information on customer transactions from 2014 to 2017. Each segment has a dedicated dashboard page that addresses the business intelligence questions raised. The SuperStore Sales Analysis project is a comprehensive data analysis tool designed to provide insights into sales data from a fictional superstore. The data was sourced from Kaggle in CSV format. Key tasks undertaken include data quality check, exploratory data analysis (EDA), statistical analysis, and visualization of trends and patterns within the dataset. Jul 16, 2024 · Contribute to Kanik-a/Superstore-Dataset-Analysis development by creating an account on GitHub. This project is an analysis of the Sample SuperStore dataset. You signed in with another tab or window. Project Goals: The goals of this project are to: Welcome to the Global Superstore Sales Analysis GitHub repository! This project focuses on utilizing Power BI visualizations to analyze the sales data of Global Superstore, a store with branches worldwide. - WuCandice/Superstore-Sales-Analysis The Superstore Sales Analysis and Prediction project aims to analyze sales data from a fictional superstore and build predictive models to forecast future sales. The goal of this project is to conduct a comprehensive analysis of the Sample Superstore dataset to gain valuable insights into Sales trends and Profitability of the store and identify areas for improvement. The analysis is based on the Superstore dataset, where we investigate sales performance, profit generation, and trends across various dimensions like product categories, regions, discounts, and time periods. I downloaded the Global Superstore Orders 2016. Leveraging Python, Streamlit, Pandas, Plotly Express,Matplotlib. The goal of the analysis is to gain insights into the store's performance and identify areas for improvement. The Superstore Sales Dataset is a popular dataset used for learning and practicing data analysis, visualization, and machine learning techniques. xlsx onto my desktop. Ship Date: Date when the order was shipped. The primary objective of this analysis is to derive insights from the Superstore’s sales data to answer the following key questions: What is the total revenue generated by the store? Which category of products contributes the most to sales? How has the sales trend been for the past year? Exploratory Data Analysis is a technique used to understand the different aspects of a dataset. This analysis aims to provide insights into the data and help identify areas where the company can increase its profits while minimizing losses. Superstore, a fictional retail entity in the U. Ship This is a sample superstore dataset, a kind of simulation where you perform extensive data analysis to deliver insights on how the company can increase its profits while minimizing the losses. Project Overview: This project analyzes a dataset of sales data from a fictional superstore. This is a practice project which I did to polish up my Excel skills. The analysis will consist of data cleaning, exploratory data analysis (EDA), a simple case of linear regression, a more complete study of multiple linear regression and finally a binary classification problem. Using the Superstore dataset, the goal of this machine learning project is to perform Exploratory Data Analysis (EDA) and implement clustering techniques to gain insights into customer behavior and optimize the store's operations. Dataset containing Sales & Profits of a Superstore. Deleted the You signed in with another tab or window. Tableau-Superstore-Analysis-Doc. The SuperStore dataset provides valuable insights into sales performance, profitability, and customer purchasing behavior. Contribute to Abu-AM/Superstore-Dataset-Analysis development by creating an account on GitHub. The project centers on a comprehensive analysis of a dataset encompassing various aspects such as sales, customer information, product details, and market segments. These insights can guide strategic decisions in inventory management, marketing, and sales optimization to enhance overall business performance. xls: This Excel file contains the Superstore sales dataset that serves as the foundation for our analysis. It simulates sales data from a fictional superstore and typically includes various attributes such as product category, sales, profit, quantity sold, customer segment, region, and order date Project Title: Data Analysis of Superstore Sales Dataset. pdf: This document offers a detailed explanation of the project, including charts and visualizations that highlight key insights derived from the Superstore dataset. The data analysis was divided into sales performance analysis, product analysis, customer analysis, and region analysis. The description of data is as follows: "Global Superstore is a customer-centric dataset, which has the data of all the You signed in with another tab or window. The dataset contains several attributes, including sales, profit, order date, ship date, and more. Analysis of Sample SuperStore Dataset. This project aims to provide valuable insights and recommendations to help Superstore Sales and Profit Analysis This repository contains the code and analysis for the "Superstore Sales and Profit" project. The project utilizes a combination of Python, SQL, and Power BI to clean the data, perform exploratory analysis, and create interactive reports. Using DAX and calculated columns, these analyses provided insights into various aspects of the superstore's operations. About. . The Superstore Sales Analysis and Prediction project aims to analyze sales data from a fictional superstore and build predictive models to forecast future sales. , specializes in furniture, office supplies, and technology products. The repository contains the following components: Sample - Superstore. Sep 16, 2024 · This project provides an insightful analysis of the Superstore dataset by performing various SQL-based operations. This repository showcases the Superstore Sales Analysis project, which aims to analyze and visualize the sales data of a fictional superstore. The sample was taken from the legendary dataset "Sample Superstore", of a fictional Ecommerce company. The data has 51290 rows, and 24 columns. This project utilizes Python libraries for data analysis and visualization, such as Pandas, Matplotlib, and Seaborn, along with machine learning models from Scikit-learn. This project involved performing EDA on the Superstore dataset using Python libraries such as Pandas, NumPy, Matplotlib, and Seaborn to gain insights into the data, identify patterns, trends, and relationships, and prepare the data for further analysis and modeling. Reload to refresh your session. Installs and libraries. The aim of this work is to analyze a dataset of purchases in an anonymous online store. This project focuses on analyzing the SuperStore dataset, which contains Sales and Profit data from a retail store in the United States. Identified and removed a duplicate transaction record for the customer Laurel Beltran using the "remove duplicate" function. You switched accounts on another tab or window. You signed out in another tab or window. S. By analysing this dataset, the project seeks to The Global Superstore dataset is a popular sample dataset often used for data analysis and visualization exercises. It is used to summarize the main characteristics of a dataset, to examine data before building model, find patterns, relations and anomalies About: The superstore data analysis project aims to gain meaningful insights from a large dataset related to a retail superstore's sales and profit. Unlocking Insights with Exploratory Data Analysis (EDA) on Superstore Dataset 🚀 Thrilled to unveil the culmination of my recent project where I dived deep into the Superstore dataset using the power trio of pandas, seaborn, and numpy! 💡 This project focuses on analyzing the SuperStore dataset, which contains sales data from a fictional retail store. The dataset contains detailed records of sales, products, customers, and shipping information. Getting Started. It generally includes the following key fields: Order ID: Unique identifier for each order. This report provides an in-depth analysis of the company's performance from 2019 to 2022, highlighting key areas of strength and potential opportunities for growth. Its main objective is to give a through understand of the data. qowhmy gln wnmrxgw iwkx mpqyw mbuse ytxkd adgoldss ucbcd enaq