R package for Customer Behavior Analysis

Multivariate Time Series Classification for Human Activity Recognition with LSTM

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.

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.

This repository contains configuration files for analysing & visualising data obtained from Southern Prefecture Restaurant.

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

Applies Principal Component Analysis (PCA) to dimensionality reduction using Python, SQL, and GBQ.

☎️ Identify customer behavior who likely to churn and make a predictive model that will classify if customer will churn or not

MusicBox user behavior(play, download, search) analysis and churn prediction(Python, Spark)

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.

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.