R package for Customer Behavior Analysis
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Updated
Apr 8, 2024 - R
R package for Customer Behavior Analysis
A Data science and Analytics project with the main aim of doing some Descriptive and Exploratory Data Analysis and then applying predictive modelling for predicting why and which are the best and most experienced employees leaving prematurely?
A Minimalistic Framework for Creating, Managing and Training Multiple Caret Models
R wrapper for MLJAR API
A set of projects I worked on as part of my PG Diploma in Data Science Program
Predicting chemosensitivity using gene expression
Presentation slides and practical examples of business forecasting using R maintained by the Centre for Marketing Analytics and Forecasting at Lancaster University
NFL is one of the most followed game having millions of followers all around the world.The current work involves the prediction of NFL match results by using a custom model incorporating a deep neural network and ticket prices for the match using regression modelling. This work considers the most important factor in NFL, the momentum. The predic…
Data Mining and Predictive Analytics : Daniel T Larose & Chantal D Larose Wiley
Machine Learning Key Projects
A predictive model that leverages classification models such as Random Forest and Naive Bayes to predict customer churn
Based on available data from bank and parameters to identify the variables that influence the most, predict the bankruptcy of the given financial model
Survival modelling using Cox proportional hazard regression model
Codes written in the course of a data science workshop at KIT in cooperation with FZI
Analyzing transactions of a retailer to predict promotional items.
The feature of interest is whether or not a customer buys a caravan insurance, based on socio-demographic factors and ownership of other insurance policies; and to build profile of a typical customer.
R Package Documentation for SOA Exam PA
Extensive Analysis and Prediction on the Bank Marketing Dataset.
lightweight toolkit to validate new observations when computing their corresponding predictions with a predictive model
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