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A collection of reference Jupyter notebooks and demo AI/ML applications for enterprise use cases: marketing, pricing, supply chain, smart manufacturing, and more.

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About

TensorHouse is a collection of reference machine learning and optimization models for enterprise operations: marketing, pricing, supply chain, and more. The goal of the project is to provide baseline implementations for industrial, research, and educational purposes.

This project contains the follwoing resources:

  • a well-documented repository of reference model implementations,
  • a manually curated list of important papers in modern operations research,
  • a manually curated list of public datasets related to entrerpirse use cases.

Illustrative Example

Strategic price optimization using reinforcement learning Price Optimization Using RL Animation

List of Models

  • Promotions and Advertisements
    • Campaign/Channel Attribution using Adstock Model
    • Customer Lifetime Value (LTV) Modeling using Markov Chain
    • Next Best Action Model using Reinforcement Learning (Fitted Q Iteration)
    • Multi-touch Multi-channel Attribution Model using Deep Learning (LSTM with Attention)
  • Search
    • Latent Semantic Analysis (LSA)
  • Recommendations
    • Nearest Neighbor User-based Collaborative Filtering
    • Nearest Neighbor Item-based Collaborative Filtering
    • Item2Vec Model using NLP Methods (word2vec)
    • Customer2Vec Model using NLP Methods (doc2vec)
  • Pricing and Assortment
    • Markdown Price Optimization
    • Dynamic Pricing using Thompson Sampling
    • Dynamic Pricing with Limited Price Experimentation
    • Price Optimization using Reinforcement Learning (DQN)
  • Supply Chain
    • Multi-echelon Inventory Optimization using Reinforcement Learning (DDPG, TD3)

Approach

  • The most basic models come from Introduction to Algorithmic Marketing book. Book's website - https://algorithmicweb.wordpress.com/
  • More advanced models use deep learning techniques to analyze event sequences (e.g. clickstream) and reinforcement learning for optimization (e.g. safety stock management policy)
  • Almost all models are based on industrial reports and real-life case studies

Contributors

  • Ilya Katsov
  • Dmytro Zikrach

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A collection of reference Jupyter notebooks and demo AI/ML applications for enterprise use cases: marketing, pricing, supply chain, smart manufacturing, and more.

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