Work-In-Progress: conjoint analysis in Python

Simple Conjoint Analyses, Tidying, and Visualization

A package for a more general, more straightforward, and more creative conjoint analysis

This repo contains files for the blog post about conjoint analysis

Syracuse University, Masters of Applied Data Science - MAR 653 Marketing Analytics

A companion plugin for Excel for charting Conjointly outputs (easy formatting for preference share and revenue charts from conjoint analysis simulations as well as colouring TURF analysis tables)

This project aims to conduct a random survey design for collecting responses regarding wine preferences of Italian consumers. Furthermore, it attempts to understand how preference share gets affected as we vary different attributes associated with wine with the use of a research method called Conjoint Analysis..

Tool: R. Conjoint analysis helps launch new products. This project helps us deeply understand how conjoint analysis gets consumers preference and segmentation, and how to simulate market to maximize profit and sustainability. It turns out that mutual cooperation yields better outcome than mutual defections.

In this project we utilize Fine Needle Aspiration data and applied machine learning technique to improve classification of benign and malignant tumor. Thereafter we use Conjoint Analysis for market positioning

Choice-Based Conjoint Analysis, Multinomial Logit Model, Multinomial Logit Model with random coefficients

This repository contains coursework for the Marketing Analytics course in the MS Applied Business Analytics program at Boston University.

Boiler template for creating conjoint choice based survey completely for free.

This repo contains old academic project codes

Explore consumer preferences & valuations for TV products with Conjoint & Bootstrapped Willingness-To-Pay analysis. Compare and analyze own design against competition for an edge! Enhance your product strategy!

Used R to build a Conjoint Analysis from scratch, assessing the importance of different features of a Flat-Panel TV.

An app to help in the calibration of Vulnerability Scores for humanitarian assistance targeting

This is a full conjoint analysis study ran on 250 survey participants, analysed through Bayesian Hierarchical Model, using RSTAN package

Replication of a Horiuchi, Smith, & Yamamoto (2020) paper on voter preferences in Japan using R for conjoint analysis.

This repository is a coursework I have taken at McCombs School of Business, UT Austin during my master's degree

This project is to predict credit card customer default risk using data from April to September 2005 for customers in Taiwan. It involves preprocessing the data, including cleaning and transforming, and employing algorithms like Apriori for association analysis and Random Forest for modeling to predict next month's defaulters.