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customer_lifetime_value_prediction

data-analysis-with-databricks-sql

designing-data-intensive-applications

digital-marketing-analytics

This contains projects based on Algorithmic Marketing like Marketing Mix Modeling, Attribution Modeling & Budget Optimization, RFM Analysis, Customer Segmentation, Recommendation Systems, and Social Media Analytics

display_parallel_slopes

A brief example of analysis the effect of a display advertising campaign using regression.

ecommerce-marketing-spend-optimization

Marketing Budget Optimization for Online Retailers using AI & Machine Learning.

getting-started-with-tensorflow

Getting Started with TensorFlow, published by Packt

github-slideshow

A robot powered training repository :robot:

hands-on-data-science-for-marketing

Hands-On Data Science for Marketing, published by Packt

hands-on-data-structures-and-algorithms-with-python-third-edition

Data Structures and Algorithms with Python – Third Edition

hands-on-exploratory-data-analysis-with-python

Hands-on Exploratory Data Analysis with Python, published by Packt

hands-on-markov-models-with-python

Hands on Markov Models with Python, published by Packt

introduction-to-data-science-in-python

lightweight_mmm

LightweightMMM 🦇 is a lightweight Bayesian Marketing Mix Modeling (MMM) library that allows users to easily train MMMs and obtain channel attribution information.

machine-learning-with-python

Python code for common Machine Learning Algorithms

mathematics-statistics-for-data-science

Mathematical & Statistical topics to perform statistical analysis and tests; Linear Regression, Probability Theory, Monte Carlo Simulation, Statistical Sampling, Bootstrapping, Dimensionality reduction techniques (PCA, FA, CCA), Imputation techniques, Statistical Tests (Kolmogorov Smirnov), Robust Estimators (FastMCD) and more in Python and R.

practical-data-analysis-using-jupyter-notebook

Practical Data Analysis using Jupyter Notebook, published by Packt Publishing

propensitytoconvert

Propensity models make true predictions about a customer’s future behavior. With propensity models you can truly anticipate a customer's future behavior. Here we focus on building a combination of a Propensity to convert and a Propensity to buy models that can influence the kind of marketing campaigns we adopt and who we decide to target (predicted converters vs non-converters) leading to spend optimizations that eventually increase the ROI on digital marketing campaigns.

tensorflow-machine-learning-cookbook

Code repository for TensorFlow Machine Learning Cookbook by Packt

tfcausalimpact

Python Causal Impact Implementation Based on Google's R Package. Built using TensorFlow Probability.

the-incredible-pytorch

The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.

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