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This repository contains assignments and projects for the course :
Algorithmic Marketing based Project to do Customer Segmentation using RFM Modeling and targeted Recommendations based on each segment
A curated list of practical financial machine learning tools and applications.
Retrieve author and publication information from Google Scholar in a friendly, Pythonic way without having to worry about CAPTCHAs!
Advanced python library to scrap Twitter (tweets, users) from unofficial API
VIP cheatsheets for Stanford's CS 229 Machine Learning
DCRMTA: Deep Causal Representation for Multi-Touch Attribution
Multi-touch attribution models to determine the channels that lead to greater customer conversions.
Python implementation of Shapley value for multi touch attribution modeling
Models for Multi-Touch Attribution
Connect the impact of marketing and your ad spend to sales. Efficiently pinpoint the impact of various revenue-generating marketing activities to understand what works best. Focus on the best-perfo…
Example Multi-Cycle, Multi-Touch Revenue and Cost Attribution Model
A curated list of awesome network analysis resources.
🎓📚📈 Collection of scientific publications that explore, model and predict customer churn and lifetime value (CLV)
🚨 Resources 💼 to learn/practice 🎯 Marketing analytics 💹 🚨
A curated list of awesome marketing tools and resources
😎 A curated list of awesome digital marketing guides, resources, services, & more.
A living document of hand-picked resources for marketers working on dev-centric products.
Compiled list of links from "Ask HN: Where can I post my startup to get beta users?"
An attention-based Recurrent Neural Net multi-touch attribution model in a supervised learning fashion of predicting if a series of events leads to conversion (purchase). The trained model can also…
Attention-RNN来做多触点归因模型
NeuralProphet: A simple forecasting package
Open source package for Survival Analysis modeling
Clustering for mixed-type data
Python Package for RFM Analysis and Customer Segmentation
Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS
LightweightMMM 🦇 is a lightweight Bayesian Marketing Mix Modeling (MMM) library that allows users to easily train MMMs and obtain channel attribution information.