alteryx / predict-customer-churn
A general-purpose framework for solving problems with machine learning applied to predicting customer churn
☆410Updated 9 months ago
Alternatives and similar repositories for predict-customer-churn:
Users that are interested in predict-customer-churn are comparing it to the libraries listed below
- A collection of demos showcasing automated feature engineering and machine learning in diverse use cases☆500Updated last year
- ☆136Updated 6 years ago
- Predict whether a loan will be repaid using automated feature engineering.☆63Updated last year
- Automated vs Manual Feature Engineering Comparison. Implemented using Featuretools.☆327Updated 4 years ago
- Predict customer lifetime value using AutoML Tables, or ML Engine with a TensorFlow neural network and the Lifetimes Python library.☆168Updated 7 months ago
- Notes and Python scripts for A/B or Split Testing☆140Updated 2 years ago
- Project work for Udacity's AB Testing Course☆82Updated 7 years ago
- A curated list of awesome customer analytics content☆95Updated 7 years ago
- edaviz - Python library for Exploratory Data Analysis and Visualization in Jupyter Notebook or Jupyter Lab☆224Updated 5 years ago
- A repository of machine learning codes written for re-usability☆144Updated 5 years ago
- Analytics and data science business case studies to identify opportunities and inform decisions about products and features. Topics incl…☆287Updated 4 months ago
- Getting start with PySpark and MLlib☆297Updated 6 years ago
- Added repo for PyData LA 2018 tutorial☆88Updated 6 years ago
- Automated feature engineering in Python with Featuretools☆515Updated 6 years ago
- Customer churn Modelling☆10Updated 6 years ago
- Material for Talk at PyData Seattle 2017☆168Updated 6 years ago
- Python implementation of the population stability index (PSI)☆139Updated last year
- HandySpark - bringing pandas-like capabilities to Spark dataframes☆192Updated 5 years ago
- Code from the book Fighting Churn With Data☆282Updated last month
- Predicting Employee Churn with Supervised Machine Learning☆65Updated 4 years ago
- Analysis for Customer Segmentation☆69Updated 4 years ago
- CausalLift: Python package for causality-based Uplift Modeling in real-world business☆348Updated last year
- A curated list of resources dedicated to Feature Engineering Techniques for Machine Learning☆589Updated 6 years ago
- Example project for the course "Testing & Monitoring Machine Learning Model Deployments"☆134Updated last year
- Improve marketing campaign of a Portuguese bank by analyzing their past marketing campaign data and recommending which customer to target☆58Updated 6 years ago
- 2nd place solution🥕🥈☆299Updated 6 years ago
- Python script (and IPython notebook) to perform RFM analysis from customer purchase history data☆270Updated 5 years ago
- Demand Forecasting Models for Kaggle competition☆81Updated 6 years ago
- Hands-On Data Science for Marketing, published by Packt☆242Updated 2 years ago
- PyCon SG 2016 - Customer Segmentation in Python☆56Updated 8 years ago