Superstore dataset github

Superstore dataset github. Superstore Sales Dataset 2015 – 2018. You can access the dataset here. Ship Superstore Sales and Profit Analysis. There are no missing values or any irrelevant data types and values. The data can be accessed via this link. This project’s data is open source, it is the Global Superstore dataset obtained from Kaggle. It contains 9993 sales transactions that occurred from 2019 to 2022. This dataset encompasses a wide range of information, including order specifics, geographical data, and product-related data. xlsx onto my desktop. The description of data is as follows: "Global Superstore is a customer-centric dataset, which has the data of all the Contribute to fxelixe/Superstore-Dataset development by creating an account on GitHub. I downloaded the Global Superstore Orders 2016. The sample was taken from the legendary dataset "Sample Superstore", of a fictional Ecommerce company. The three categories all account for over 30% of sales Here's an Exploratory data analysis process on the superstore dataset. Ship Date: Date when the order was shipped. Jan 17, 2021 · The Super Store dataset contains data on order details of customers for orders of a superstore in the US. The following code is written in Python and mainly uses Pandas, NumPy, Matplotlib and Seaborn libraries. The data was sourced from Kaggle in CSV format. Importing the necessary libraries, the dataset and visualizing the first 5 rows; Visualizing the last five rows and explaining the columns available in the dataset The SuperStore Database Management Project (DBMD) is a comprehensive solution designed to streamline and optimize the operations of an e-commerce business. The dataset includes order details, anonymized customer information, product specifics, and financial metrics. The Global Superstore dataset is a popular sample dataset often used for data analysis and visualization exercises. 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. Identified and removed a duplicate transaction record for the customer Laurel Beltran using the "remove duplicate" function. Contribute to larryt2003/Superstore-Sales-Dataset-2015-2018 development by creating an account on GitHub. You switched accounts on another tab or window. Superstore Sales and Profit Analysis This repository contains the code and analysis for the "Superstore Sales and Profit" project. Order Date: Date when the order was placed. The analyses cover a range of business insights, including sales performance, customer segmentation, and product profitability. The analysis is based on the Superstore dataset, where we investigate sales performance, profit generation, and trends across The company should focus more on technology section because the profit is higher in the that cateogory; Since furniture is not contributing towards any profit , so the company can stop selling furniture . This project focuses on creating a robust database management system that facilitates efficient handling of various aspects of an online store, from product inventory to customer orders. Reload to refresh your session. This is the Python version analysis approach, towards the legendary Sample Superstore Dataset with Pandas - leonism/sample-superstore The original dataset was obtained from the HiCounselor website. The data has 51290 rows, and 24 columns. By analysing this dataset, the project seeks to 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. Quickly spot increases or decreases in sales, profit ratio, and shipping time, and switch to the Prescriptive tab to pinpoint the root cause. - GitHub - The Superstore Analysis project aims to provide comprehensive insights into the performance, trends, and patterns within the sales data of a fictional superstore. Dataset Preparation: The Superstore sales data is obtained and imported into Power Query for data cleaning and transformation. This dataset pertains to a superstore in the US, providing information on customer transactions from 2014 to 2017. The SuperStore dataset comprises a comprehensive sales record from a superstore, containing 9,994 entries across 19 distinct fields. You signed out in another tab or window. The Superstore Sales Dataset is a popular dataset used for learning and practicing data analysis, visualization, and machine learning techniques. In Sep 10, 2024 · In this article, I will guide you through the creation of a sales analysis dashboard using the popular “Superstore Sales” dataset. This includes the state, region, order date, shipping date, product ordered etc. You signed in with another tab or window. This repository showcases various data analyses on the popular Superstore dataset using SQL queries. Data Cleaning and Transformation: Using Power Query, the data is cleaned by removing irrelevant columns, filtering out unnecessary data, and handling missing values. This project uses the Global Superstore dataset, which is open source and available on Kaggle. The SuperStore Dataset 2019-2022 contains 9,994 sales records across 19 fields, detailing orders, customers, products, and financial metrics, providing insights into regional sales, product categories, and customer behavior. . Using the Superstore dataset provided, create a dashboard consisting of at least 2 KPIs, you identified in Module 2, that will be used to answer the business question for this project. This notebook is intended for those whose relatively new to EDA (Exploratory Data Analysis) aspect from a Machine Learning process. ~dashboard showcase~ Super Sample Superstore. Perform ‘Exploratory Data Analysis’ on dataset ‘SampleSuperstore’ This is a practice project which I did to polish up my Excel skills. Superstore Sales and Profit Analysis. Utilizing Power BI, this project delves into various aspects such as overall performance, category/sub-category analysis, sales trends, profitability, return analysis, regional About: The superstore data analysis project aims to gain meaningful insights from a large dataset related to a retail superstore's sales and profit. Given the insights gained from the EDA, the superstore can choose to remove non-profitable products or invest in marketing efforts for products, segments and geographical areas that are driving their profit. This corporate style viz is a different take on the classic Tableau Superstore data set. 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 Dataset containing Sales & Profits of a Superstore. It generally includes the following key fields: Order ID: Unique identifier for each order. This report analyzes various aspects of the dataset to extract meaningful insights. Categories and sub categories. Deleted the . Each analysis is documented with the SQL queries used and explanations of the steps involved. The dataset contains several attributes, including sales, profit, order date, ship date, and more. The dataset is in CSV format, comprising 51,290 observations and 24 features. The dataset is in a CSV format with 51,290 observations and 24 features. The analysis is based on the Superstore dataset, where we investigate sales performance, profit generation, and trends across I mainly chose this data set to analyze how a store work under different situations and how they tackle profit or loss, while they are in profit how they got profit and how to increase it further, and if they are in loss how they tend to increase the sales and background works like how they analyze to make profit and improvement in sales. dyfwh gym mtgdza jeniodp dxnhre otg yorr vtqmv cimgn zjduhb