Topic: customer-behavior Goto Github
Some thing interesting about customer-behavior
Some thing interesting about customer-behavior
customer-behavior,This repository contains the analysis of Iowa liquor retail sales data, aimed at uncovering sales trends and forecasting future sales patterns. The project involves data cleaning, preparation, and advanced time series analysis using Microsoft SQL Server and Google Colab.
User: 1401dev
customer-behavior,Multivariate Time Series Classification for Human Activity Recognition with LSTM
User: anaramirli
customer-behavior,This repository contains configuration files for analysing & visualising data obtained from Southern Prefecture Restaurant.
User: cherukuri-thanu
customer-behavior,The project provides the Apriori algorithm and Market Basket Analysis (MBA) to analyze transactional data, generating personalized recommendations based on Support, Confidence, and Lift metrics to enhance customer experience and boost sales.
User: damaniayesh
customer-behavior,Leveraging K-Means clustering, our project categorizes retail customers based on purchasing behaviors and demographics. This provides businesses with actionable insights to tailor marketing efforts, enhancing customer experience and boosting sales.
User: esana1
customer-behavior,☎️ Identify customer behavior who likely to churn and make a predictive model that will classify if customer will churn or not
User: graciangelica
customer-behavior,MusicBox user behavior(play, download, search) analysis and churn prediction(Python, Spark)
User: joyceft
customer-behavior,This notebook focuses on RFM (Recency, Frequency, Monetary) segmentation, a popular method used in customer analysis to group customers based on their purchasing behavior. The key goal of RFM segmentation is to identify different customer segments by analyzing their transaction history and assigning them to categories based on their recency of purc
User: m3ghan
customer-behavior,R package for Customer Behavior Analysis
User: mplatzer
Home Page: https://cran.r-project.org/web/packages/BTYDplus
customer-behavior,Applies Principal Component Analysis (PCA) to dimensionality reduction using Python, SQL, and GBQ.
User: xin-bu
customer-behavior,Key: clustering, using logistic regression to build elasticity modeling for purchase probability, brand choice, and purchase quantity & deep neural network to build a black-box model to predict future customer behaviors.
User: zhangkelly014
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