The Consumer Price Index (CPI) is a measure of the average change over time in the prices paid by a representative consumer for a common basket of goods and services. The CPI attempts to quantify and measure the average cost-of-living in a given country by estimating the purchasing power of a single unit of currency. Therefore, it is the key macroeconomic indicator for measuring inflation (or deflation). As such, the CPI is a major driving force in the economy influencing a plethora of market dynamics. In this project, I present a novel model based on Time Series Analysis for forecasting CPI.

Instacart Market Basket Analysis: Which products will an Instacart consumer purchase again?

Instacart Market Basket Analysis - Which products will an Instacart consumer purchase again?

Решения соревнования Анализ потребительской корзины на платформе kaggle в рамках курса DMIA Спорт

Advanced Consumer Basket Analysis with Dashboard & Forecasting A data analytics project using Python, Streamlit, and Prophet to analyze supermarket sales. Includes customer segmentation (KMeans), interactive dashboard visualizations, profitability insights, and a 30-day sales forecast.

This project was done to create a Market Basket Analysis, identify consumer buying trends, and identify the items that are combined utilising Association Rules. Data from a supermarket's sales were used in the study.

Enhanced seller strategies on Instacart by uncovering consumer behavior through advanced Market Basket Analysis. Implemented Apriori, FP-Growth, and Eclat algorithms, comparing outcomes for effective product bundling and inventory management. Explored association rules to optimize suggested product combinations.

Basket analysis is crucial for Direct-to-Consumer (D2C) brands because it enables them to identify product combinations that are frequently purchased together.This helps brands personalize product recommendations based on frequently bought items, driving upselling and cross-selling strategies.

Today, I embarked on an exciting journey by building a new project: the Big Basket-Analysis Project! 🎉 This initiative aims to dive deep into consumer behavior and trends in the grocery sector. By analyzing data from various sources, I hope to uncover insights that can drive strategic decisions and improve customer experiences.

One of the most motivating areas of research in data mining that continuously becomes popular is Sales Pattern Recognition or supermarket analysis. With the rapid development of economy and information technology, the development of the market retail industry cannot be underestimated. For discovering new trends, mining plays an important role in supermarket analysis which is helpful for all parties associated with this field. The process of data mining is to extract data by automatic or semi-automatic means. Here, Apriori algorithm is used to find out the data of shopping basket from the massive data of consumers reveals the relationship between the purchased goods, and subsequently applies the association rules and CART decision tree algorithm to reveal the characteristics of the customer group and the target customers classification. In order to dig out more detailed and valuable information, it is convenient for the goods to be better configured and sold, and to improve the operational efficiency of the market.

Consumer Market Basket Simulation and Analysis

Affinity analysis on consumers' baskets using R.

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Instacart Market Basket Analysis. Which products will an Instacart consumer purchase again?

Instacart Market Basket Analysis Which products will an Instacart consumer purchase again?

Unveiling Consumer Insights: Market Basket Analysis in E-Commerce through Machine Learning Algorithms

Unveiling Consumer Insights: Market Basket Analysis in E-Commerce through Machine Learning Algorithms

Market Basket analysis is used to uncover links between consumer items purchased together from retailers

Market Basket / Consumer Behavior Analysis on Groceries Dateset for maximizing sales based on item pairing

Consumer behavior analysis on 3M+ Instacart transactions: RFM segmentation, K-Means clustering, Market Basket Analysis, and retention experiment simulation

Market Basket Analysis for Consumer Item, Where the Customer are Dealer and Distributor of the Company

Applied Apriori algorithm for in-depth analysis of market basket data, extracting insights into consumer behavior.

This project used the Apriori algorithm to perform market basket analysis to better understand consumer behaviour in shopping markets

The goal of Market Basket Analysis is to understand consumer behavior by identifying relationships between the items that people buy

Implementation of Market Basket Analysis, a modeling technique used by retailers to uncover associations or patterns between items bought by consumers.

To identify profitable product bundles and personalized recommendations for specialty coffee consumers based on their purchasing patterns using market basket analysis.

In this project, apriori algorithm has been used to explain and foretell consumer purchasing behaviors, commonly known as Market Basket Analysis.