Superstore dataset analysis python

Superstore dataset analysis python. The SuperStore Sales Analysis project is a comprehensive data analysis tool designed to provide insights into sales data from a fictional superstore. Follow. This Feb 23, 2023 · The superstore dataset was downloaded from kaggle using phyton, furthermore, library for deep analysis of this project used pandas, matplotlib and seaborn. The main objectives are to identify sales trends, understand customer behavior, optimize operational efficiency, and improve profitability. This Notebook is running on top of the following stacks : Python 3. This repository contains a comprehensive analysis of the Superstore dataset using Python in Jupyter Notebook and Power BI. Jul 30, 2024 · The superstore dataset provides sales and profit data for a variety of products across different categories and regions. Sales Analysis: DIFFICULTY: MEDIUM. The dataset includes order details, anonymized customer information, product specifics, and financial metrics. This repository showcases the Superstore Sales Analysis project, which aims to analyze and visualize the sales data of a fictional superstore. Apr 24, 2023 · <h2id="3">How to Perform Market Basket Analysis in Python? Now that you understand how the Apriori algorithm works, let us perform market basket analysis in Python using Kaggle’s Grocery Dataset. - sersun/supermarket-sales-analysis Dec 16, 2023 · Analysis of Superstore Sales Dataset. — As Explore and run machine learning code with Kaggle Notebooks | Using data from Superstore Sales Dataset SuperStore Sales Analysis in Python | Kaggle Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. The sample was taken from the legendary dataset "Sample Superstore", of a fictional Ecommerce company. This notebook is intended for those whose relatively new to EDA (Exploratory Data Analysis) aspect from a Machine Learning process. 4 Explore and run machine learning code with Kaggle Notebooks | Using data from Superstore Dataset 🍔 EDA Superstore Analysis 📈 Python | Kaggle Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. 0. Share. I have used Python libraries pandas and numpy for analysis here. I start by loading the data in a dataframe in a csv using Python pandas library, followed by importing the required libraries and proceeded to prepare it for further analysis. Sales and Profit Correlation (0. This report analyzes various aspects of the dataset to extract meaningful insights. The Superstore dataset represents fictional sales data for a retail superstore, and the analysis aims to gain insights into various aspects of the store's performance. Aug 24, 2023 · Analysis: 1. Perform ‘Exploratory Data Analysis’ on dataset ‘SampleSuperstore’ Explore and run machine learning code with Kaggle Notebooks | Using data from US Superstore data Jan 17, 2021 · The Super Store dataset contains data on order details of customers for orders of a superstore in the US. Here, the top 5 rows Nov 15, 2023 · Learn how to analyze supermarket data using Python, from data collection and cleaning to exploratory data analysis, market basket analysis, customer segmentation, sales and revenue analysis, inventory management, pricing analysis, predictive analytics, and recommendation systems. The repository contains the following components: Feb 18, 2024 · The Superstore Data Analysis Project focuses on extracting insights from a retail superstore dataset. The goal of this project is to analyse the data and identify Jan 15, 2020 · An introduction to Sample SuperStore Dataset Walkthrough, Using Python and the Pandas Library, while utilizing Jupyter Notebook as the IDE. Superstore, a fictional retail entity in the U. Jul 9, 2021 · Conduct an exploratory data analysis by analysing Super Store sales data and identifying opportunities to boost business growth This notebook is intended for those whose relatively new to EDA (Exploratory Data Analysis) aspect from a Machine Learning process. Download the dataset before you start coding along with this tutorial. Analyze sales data from a supermarket dataset to uncover insights into customer behavior, product performance, and operational efficiency. ️ Reference links ~dashboard showcase~ Super Sample Superstore This corporate style viz is a different take on the classic Tableau Superstore data set. The three categories all account for over 30% of sales 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. Explore and run machine learning code with Kaggle Notebooks | Using data from superstore_data SuperStore Dataset Analysis | Kaggle Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. 7; Pandas 1. Oct 29, 2023 · According to the distribution, Office Suppliers is the most popular product category among Americans, with a frequency of approximately 3600, implying that Americans purchased things in the Office The dataset contains records of orders from a fictional retail store, including information about products, customers, and their respective sales transactions. 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. Categories and sub categories. The sample was taken from the legendary dataset “Sample Superstore”, of a fictional Ecommerce company. Explore trends, patterns, and key metrics to inform strategic business decisions and drive growth. The project utilizes a combination of Python, SQL, and Power BI to clean the data, perform exploratory analysis, and create interactive reports. S. Quickly spot increases or decreases in sales, profit ratio, and shipping time, and switch to the Prescriptive tab to pinpoint the root cause. Gold The project focuses on the analysis of sales and profit from the Superstore dataset using Python libraries and visualization tools, like NumPy, Pandas, Matplotlib and Seaborn. This is the flow in flow chart. 61): — A positive correlation of 0. RFM modeling was done by manually assigning customer loyalty groups to various combinations of R/F/M values ranked from 1-4 (4 being the highest). Sample SuperStore. The “Superstore Sales & Profit Analysis” project aims to uncover valuable insights and trends within the Superstore Dataset obtained from Kaggle. , specializes in furniture, office supplies, and technology products. Mar 30, 2022 · You will learn how to run EDA and perform it on the superstore dataset by using Python and then, visualising and analysing the data. Flow Chart by Kelechi Uzoukwu May 25, 2023 · Cleaning the data involves manipulating, removing duplicates and handling null/missing values in the dataset. 3 min read · Dec 16, 2023--Listen. The dataset contains information about sales, customers, products, and orders from a fictional The SuperStore dataset comprises a comprehensive sales record from a superstore, containing 9,994 entries across 19 distinct fields. The analysis involves various stages, including data cleaning, feature engineering, and model training to predict sales. 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. 61 between sales and profit suggests a moderately strong positive relationship between these two variables. Step 1: Pre-Requisites for Performing Market Basket Analysis. Data source: Superstore Sales Dataset — https: Python. Explore and run machine learning code with Kaggle Notebooks | Using data from Superstore Sales Dataset Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Leveraging Python, Streamlit, Pandas, Plotly Express,Matplotlib. . We started by visualizing the data to identify trends and patterns. 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. Use lambda functions to establish Recency/Frequency/Monetary columns Split columns Jul 29, 2023 · Photo by Adrien Delforge on Unsplash. The dataset required basic data cleaning and restructuring via Python. - WuCandice/Superstore-Sales-Analysis May 4, 2023 · Step 3: Exploratory Data Analysis (EDA) After cleaning the data, we performed EDA by using Python Jupyter Notebook. tqd qlt jza juwrn eycm wvnnj mzvc evbjz mbjpo rpoq