The growth of supermarkets in most populated cities are increasing and market competitions are also high. This dataset is one of the historical sales of supermarket company which has recorded in 3 different branches for 3 months data. Predictive data analytics methods are easy to apply with this datasets.

This is a business intelligence project on analyzing super market data. Check out the README file for more details.

MySQL Script to Create Reports for Multiple Business Scenarios

In developmental robotics, robot learning algorithms generate their own sequences of learning experiences, also known as a curriculum, to cumulatively acquire new skills through self-guided exploration and social interaction with humans. These robots use guidance mechanisms such as active learning, maturation, motor synergies and imitation. Association rules Main article: Association rule learning See also: Inductive logic programming Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness".[60] Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction.[61] Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems. Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets.[62] For example, the rule {\displaystyle \{\mathrm {onions,potatoes} \}\Rightarrow \{\mathrm {burger} \}}\{{\mathrm {onions,potatoes}}\}\Rightarrow \{{\mathrm {burger}}\} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis, association rules are employed today in application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions. Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.[63] Inductive logic programming (ILP) is an approach to rule-learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples. Inductive programming is a related field that considers any kind of programming language for representing hypotheses (and not only logic programming), such as functional programs. Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting.[64][65][66] Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples.[67] The term inductive here refers to philosophical induction, suggesting a theory to explain observed facts, rather than mathematical induction, proving a property for all members of a well-ordered set. Models Performing machine learning involves creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems. Artificial neural networks Main article: Artificial neural network See also: Deep learning An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another. Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68]

This repo houses my notebooks on Exploratory data analysis as well as feature engineering and Modelling of a Supermarket dataset to predict sales provided by the Data science Nigeria body for the intercampus machine learning competition july to september 2018

Performed data cleaning, visualization, and analysis on supermarket sales data using Python libraries. Identified sales trends, customer behavior patterns, and product performance insights

Project on Supermarket sales Analysis using Python to carry out data wrangling and Exploratory data ANALYSIS(EDA) along side dataViz respectively shown on Power bi.

Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness.[1] Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami[2] introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets. For example, the rule {\displaystyle \{\mathrm {onions,potatoes} \}\Rightarrow \{\mathrm {burger} \}}\{{\mathrm {onions,potatoes}}\}\Rightarrow \{{\mathrm {burger}}\} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as, e.g., promotional pricing or product placements. In addition to the above example from market basket analysis association rules are employed today in many application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions.

Exploratory Data Analysis Project on Historical Supermarket Sales Data

This project focuses on utilizing historical sales data from three different supermarkets to predict the gross income. The dataset contains information on various aspects of sales transactions, such as invoice details, branch locations, etc.

The Supermarket Sales Dashboard provides a detailed overview of sales performance, profit trends, product and category contributions, and customer preferences in payment modes. This interactive dashboard helps businesses monitor key sales indicators and optimize decision-making for inventory, pricing, and promotions.

To perform Exploratory Data Analysis (EDA) on a supermarket sales dataset. It will be accomplised by completing each task in the project: Task 1: Initial Data Exploration Task 2: Univariate Analysis Task 3: Bivariate Analysis Task 4: Dealing With Duplicate Rows and Missing Values Task 5: Correlation Analysis

Exploratory Data Analysis on some supermarket data provided by DSN

Web app to explore supermarket products with advanced filters, search, favorites, and nutritional info. Includes data analysis notebooks for deeper insights.

Problem Statement: This data set is created only for the learning purpose of the customer segmentation concepts , also known as market basket analysis . I will demonstrate this by using unsupervised ML technique (KMeans Clustering Algorithm) in the simplest form.You are owing a supermarket mall and through membership cards , you have some basic data about your customers like Customer ID, age, gender, annual income and spending score. Problem Statement You own the mall and want to understand the customers like who can be easily converge [Target Customers] so that the sense can be given to marketing team and plan the strategy accordingly.

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End-to-End Exploratory Data Analysis (EDA) on supermarket sales data using Python, Pandas, matplotlib,seaborn,jupyter and postgresql,pgadmin

The Supermarket Sales Dashboard offers an in-depth view of sales performance, profit patterns, product and category contributions, and customer payment preferences. This interactive tool helps businesses track essential sales metrics and make informed decisions about inventory, pricing, and promotional strategies.

This is the Analysis of supermarket sales dataset using Python to get an insight on the sales. Here I use Python Libraries - Pandas, Numpy and Seaborn to analyze the dataset. This is done as a part of Main Project for the Data Analytics Course provided by EduBridge.

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This project analyzes supermarket sales data from three Chinese cities. After preprocessing and performing descriptive analysis (univariate/bivariate), we developed a linear regression model to predict sales trends based on variables like product type and customer demographics.

I have used Tableau and OrangeML to conduct exploratory data analysis, data visualization and predictive analytics to find insights from 3-month sales data of a supermarket in Mayanmar.

Welcome to the "Supermarket Sales Analysis" repository! This repository is dedicated to showcasing a comprehensive analysis of supermarket sales data using a combination of Python, SQL, pivot tables, and dashboard visualization techniques.you'll find valuable insights and resources within this repository.

An analysis of a Giant retail supermarket chain's sales data for a period of 2.5 years using R to build a model for forecasting Sales

SQL-based analysis of supermarket sales data. Project includes data cleaning, querying, and insights on customer behavior, product performance, and revenue trends. Covers advanced SQL techniques like joins, aggregation, subqueries, and window functions to drive business decisions.

The "SuperStore Sales Dashboard with Live Forecasting" is a data analytics and visualization project aimed at providing valuable insights and accurate sales forecasting to support the growth and efficiency of a supermarket. This project combines data analysis techniques, interactive dashboard design, and time series analysis to empower decision-mak

The retail industry, particularly supermarkets, relies heavily on sales data analysis to make informed decisions about staffing, inventory management, and overall business strategies. In this project, we aim to conduct a comprehensive analysis of sales data from a supermart to uncover valuable insights that can drive business growth and efficiency.

This beginner-friendly project covers the complete Power BI workflow—from data import and cleaning to DAX measures and dashboard design. It visualizes key metrics like total sales, product performance, and customer trends for retail decision-making.

The objective of this project is to analyze supermarket sales using pivot table and chart in Microsoft Excel. After doing the analysis, I created an interactive dashboard that aims to make it easier for the audience to explore data.