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anomaly-detection-walmart

anomalydetection

Anomaly Detection with R

big-data-analytics-canvas

We propose a new methodology to support organization succeed in their transition toward data-driven decisions. Big Data & Advanced analytics projects are very complex by nature, the Big Data Analytics Canvas provides an helicopter view organized around 4 main steps: 1) Data Integration, 2) Data Exploration, 3) Insights Generation, and 4) Decisions Optimzation.

catboost

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

causalimpact

An R package for causal inference in time series

causalml

Uplift modeling and causal inference with machine learning algorithms

causalnex

A Python library that helps data scientists to infer causation rather than observing correlation.

cortana-intelligence-price-optimization

Retail industry solutions for product price optimization using the Cortana Intelligence Suite with end-to-end walkthrough

deep-reinforcement-learning-aap

DeepAir Solutions : Price recommendations for ancillary facilities for airline using deep reinforcement learning. AAP refers to Airline Ancillary Pricing.

deep-reinforcement-learning-algorithm-for-dynamic-pricing-of-express-lanes-with-multiple-access-loca

efficient_frontier

Markowitz portfolio optimisation (efficient frontier) in Python

kedro

A Python library that implements software engineering best-practice for data and ML pipelines.

kedro-viz

Visualise your Kedro data pipelines.

marketing-data-science

Analytics and data science business case studies to identify opportunities and inform decisions about products and features. Topics include Markov chains, A/B testing, customer segmentation, and machine learning models (logistic regression, support vector machines, and quadratic discriminant analysis).

marketing_mixed_modelling_analysis

Fitted a multivariate regression model on a brand’s product Sales Volume and the availabe marketing time series data to (i.e. Advertising, Distribution, Pricing) to estimate the impact of various marketing tactics on sales and then forecast the impact of future sets of tactics.

minimizing-electricity-cost-with-model-based-deep-rl

This project is about exploring the use of model-based reinforcement learning with Bayesian neural networks to minimize the electricity cost for electricity consumers who have their own photovoltaic system and a battery. The method used here is designed for environments with dynamic electricity prices.

mmm_stan

Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS

neural_prophet

NeuralProphet - A simple forecasting model based on Neural Networks in PyTorch

price-optimization-shiny

Shiny app for Price Optimization using prophet and lme4 libraries for R.

stochastic_opt_ot

implementation of the paper "Stochastic Optimization for Large-scale Optimal Transport" (https://arxiv.org/pdf/1605.08527.pdf).

tensor-house

A collection of reference machine learning and optimization models for enterprise operations: marketing, pricing, supply chain

the-pricing-power-playbook

6 steps to get Pricing Power under control.

timecop

Time series based anomaly detector