Rule-based Customer Segmentation

Last update: Jan 03, 2022

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Overview

Rule-based Customer Segmentation

Business Problem

A game company wants to create level-based new customer definitions (personas) by using some features of its customers, and to create segments according to these new customer definitions and to estimate how much the new customers can earn on average according to these segments.

For Example: How much money come from it's customer who is 25-year-old, male and from Turkey, and he is an IOS user.

Dataset

Persona.csv dataset contains the prices of the products sold by an international game company and some demographic information of the users who buy these products.The data set consists of records created in each sales transaction.This means that the table is not deduplicated.In other words, a user with certain demographic characteristics may have made more than one purchase.

Owner

Cem Çaluk

Data Science

GitHub Repository

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Rule-based Customer Segmentation

Related tags

Overview

Rule-based Customer Segmentation

Business Problem

Dataset

Owner

Cem Çaluk

AAAI 2022 paper - Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction

Image super-resolution (SR) is a fast-moving field with novel architectures attracting the spotlight

Fuzzing JavaScript Engines with Aspect-preserving Mutation

pytorch implementation of the ICCV'21 paper "MVTN: Multi-View Transformation Network for 3D Shape Recognition"

Code for "NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video", CVPR 2021 oral

CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation

Perform zero-order Hankel Transform for an 1D array (float or real valued).

Code for EMNLP 2021 paper: "Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training"

Python 3 module to print out long strings of text with intervals of time inbetween

Unofficial keras(tensorflow) implementation of MAE model from Masked Autoencoders Are Scalable Vision Learners

CoSMA: Convolutional Semi-Regular Mesh Autoencoder. From Paper "Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different Sizes"

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