icp4d-customer-churn-classifier - Infuse AI into your application

  •        21

In this code pattern, we will create and deploy a customer churn prediction model using IBM Cloud Private for Data. First, we will load customer demographics and trading activity data into Db2 Warehouse. Next, we'll use a Jupyter notebook to visualize the data, build hypotheses for prediction, and then build, test, and save a prediction model. Finally, we will enable a web service and use the model from an app. The use case describes a stock trader company that can use churn prediction to target offers for at-risk customers. Once deployed, the model can be used for inference from an application using the REST API. A simple app is provided to demonstrate using the model from a Python app.

https://developer.ibm.com/patterns/infuse-ai-into-your-application/
https://github.com/IBM/icp4d-customer-churn-classifier

Tags
Implementation
License
Platform

   




Related Projects

spark-py-notebooks - Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks

  •    Jupyter

This is a collection of IPython notebook/Jupyter notebooks intended to train the reader on different Apache Spark concepts, from basic to advanced, by using the Python language. If Python is not your language, and it is R, you may want to have a look at our R on Apache Spark (SparkR) notebooks instead. Additionally, if your are interested in being introduced to some basic Data Science Engineering, you might find these series of tutorials interesting. There we explain different concepts and applications using Python and R.

Agile_Data_Code_2 - Code for Agile Data Science 2.0, O'Reilly 2017, Second Edition

  •    Jupyter

Like my work? I am Principal Consultant at Data Syndrome, a consultancy offering assistance and training with building full-stack analytics products, applications and systems. Find us on the web at datasyndrome.com. There is now a video course using code from chapter 8, Realtime Predictive Analytics with Kafka, PySpark, Spark MLlib and Spark Streaming. Check it out now at datasyndrome.com/video.

business-machine-learning - A curated list of practical business machine learning (BML) and business data science (BDS) applications for Accounting, Customer, Employee, Legal, Management and Operations (by @firmai)

  •    Jupyter

Animated Investment Management Research at Sov.ai — Sponsoring open source AI, Machine learning, and Data Science initiatives. A curated list of applied business machine learning (BML) and business data science (BDS) examples and libraries. The code in this repository is in Python (primarily using jupyter notebooks) unless otherwise stated. The catalogue is inspired by awesome-machine-learning.

benchm-ml - A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc

  •    R

This project aims at a minimal benchmark for scalability, speed and accuracy of commonly used implementations of a few machine learning algorithms. The target of this study is binary classification with numeric and categorical inputs (of limited cardinality i.e. not very sparse) and no missing data, perhaps the most common problem in business applications (e.g. credit scoring, fraud detection or churn prediction). If the input matrix is of n x p, n is varied as 10K, 100K, 1M, 10M, while p is ~1K (after expanding the categoricals into dummy variables/one-hot encoding). This particular type of data structure/size (the largest) stems from this author's interest in some particular business applications. Note: While a large part of this benchmark was done in Spring 2015 reflecting the state of ML implementations at that time, this repo is being updated if I see significant changes in implementations or new implementations have become widely available (e.g. lightgbm). Also, please find a summary of the progress and learnings from this benchmark at the end of this repo.

ml-workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.

  •    Jupyter

The ML workspace is an all-in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on your own machines. This workspace is the ultimate tool for developers preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch, Keras, Sklearn) and dev tools (e.g., Jupyter, VS Code, Tensorboard) perfectly configured, optimized, and integrated. The workspace requires Docker to be installed on your machine (📖 Installation Guide).


swift-sdk - :iphone: The Watson Swift SDK enables developers to quickly add Watson Cognitive Computing services to their Swift applications

  •    Swift

The Watson Developer Cloud Swift SDK makes it easy for mobile developers to build Watson-powered applications. With the Swift SDK you can leverage the power of Watson's advanced artificial intelligence, machine learning, and deep learning techniques to understand unstructured data and engage with mobile users in new ways. This SDK provides classes and methods to access the following Watson services.

industry-machine-learning - A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai)

  •    Jupyter

Animated Investment Management Research at Sov.ai — Sponsoring open source AI, Machine learning, and Data Science initiatives. Have a look at the newly started FirmAI Medium publication where we have experts of AI in business, write about their topics of interest.

practical-machine-learning-with-python - Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system

  •    Jupyter

"Data is the new oil" is a saying which you must have heard by now along with the huge interest building up around Big Data and Machine Learning in the recent past along with Artificial Intelligence and Deep Learning. Besides this, data scientists have been termed as having "The sexiest job in the 21st Century" which makes it all the more worthwhile to build up some valuable expertise in these areas. Getting started with machine learning in the real world can be overwhelming with the vast amount of resources out there on the web. "Practical Machine Learning with Python" follows a structured and comprehensive three-tiered approach packed with concepts, methodologies, hands-on examples, and code. This book is packed with over 500 pages of useful information which helps its readers master the essential skills needed to recognize and solve complex problems with Machine Learning and Deep Learning by following a data-driven mindset. By using real-world case studies that leverage the popular Python Machine Learning ecosystem, this book is your perfect companion for learning the art and science of Machine Learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute Machine Learning systems and projects successfully.

Math-of-Machine-Learning-Course-by-Siraj - Implements common data science methods and machine learning algorithms from scratch in python

  •    Jupyter

This repository was initially created to submit machine learning assignments for Siraj Raval's online machine learning course. The purpose of the course was to learn how to implement the most common machine learning algorithms from scratch (without using machine learning libraries such as tensorflow, PyTorch, scikit-learn, etc). Although that course has ended now, I am continuing to learn data science and machine learning from other sources such as Coursera, online blogs, and attending machine learning lectures at University of Toronto. Sticking to the theme of implementing machine learning algortihms from scratch, I will continue to post detailed notebooks in python here as I learn more.

evidently - Interactive reports to analyze machine learning models during validation or production monitoring

  •    Jupyter

Interactive reports and JSON profiles to analyze, monitor and debug machine learning models. Evidently helps evaluate machine learning models during validation and monitor them in production. The tool generates interactive visual reports and JSON profiles from pandas DataFrame or csv files. You can use visual reports for ad hoc analysis, debugging and team sharing, and JSON profiles to integrate Evidently in prediction pipelines or with other visualization tools.

dive-into-machine-learning - Dive into Machine Learning with Python Jupyter notebook and scikit-learn!

  •    

I learned Python by hacking first, and getting serious later. I wanted to do this with Machine Learning. If this is your style, join me in getting a bit ahead of yourself. I suggest you get your feet wet ASAP. You'll boost your confidence.

data-science-your-way - Ways of doing Data Science Engineering and Machine Learning in R and Python

  •    Jupyter

These series of tutorials on Data Science engineering will try to compare how different concepts in the discipline can be implemented in the two dominant ecosystems nowadays: R and Python. We will do this from a neutral point of view. Our opinion is that each environment has good and bad things, and any data scientist should know how to use both in order to be as prepared as posible for job market or to start personal project.

python-machine-learning-book-2nd-edition - The "Python Machine Learning (2nd edition)" book code repository and info resource

  •    Jupyter

Python Machine Learning, 2nd Ed. To access the code materials for a given chapter, simply click on the open dir links next to the chapter headlines to navigate to the chapter subdirectories located in the code/ subdirectory. You can also click on the ipynb links below to open and view the Jupyter notebook of each chapter directly on GitHub.

Data-Analysis-and-Machine-Learning-Projects - Repository of teaching materials, code, and data for my data analysis and machine learning projects

  •    Jupyter

This is a repository of teaching materials, code, and data for my data analysis and machine learning projects.Each repository will (usually) correspond to one of the blog posts on my web site.

machine-learning - :earth_americas: machine learning algorithms tutorials (mainly in Python3)

  •    HTML

This is a continuously updated repository that documents personal journey on learning data science, machine learning related topics. Forecasting methods for timeseries-based data.

machine-learning-asset-management - Machine Learning in Asset Management (by @firmai)

  •    Jupyter

Follow this link for SSRN paper. Animated Investment Management Research at Sov.ai — Sponsoring open source AI, Machine learning, and Data Science initiatives.

python-business-analytics - Python solutions to solve practical business problems.

  •    Jupyter

Animated Investment Management Research at Sov.ai — Sponsoring open source AI, Machine learning, and Data Science initiatives. A series looking at implementing python solutions to solve practical business problems. Share your own projects on this subreddit, r/datascienceproject. Every week we will look at hand picked businenss solutions. See the following google drive for all the code and github for all the data. If you follow the LinkedIn page, you would be able to see the lastest developments.

python-machine-learning-book - The "Python Machine Learning (1st edition)" book code repository and info resource

  •    Jupyter

This GitHub repository contains the code examples of the 1st Edition of Python Machine Learning book. If you are looking for the code examples of the 2nd Edition, please refer to this repository instead. What you can expect are 400 pages rich in useful material just about everything you need to know to get started with machine learning ... from theory to the actual code that you can directly put into action! This is not yet just another "this is how scikit-learn works" book. I aim to explain all the underlying concepts, tell you everything you need to know in terms of best practices and caveats, and we will put those concepts into action mainly using NumPy, scikit-learn, and Theano.

MMLSpark - Microsoft Machine Learning for Apache Spark

  •    Scala

MMLSpark provides a number of deep learning and data science tools for Apache Spark, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK) and OpenCV, enabling you to quickly create powerful, highly-scalable predictive and analytical models for large image and text datasets.MMLSpark requires Scala 2.11, Spark 2.1+, and either Python 2.7 or Python 3.5+. See the API documentation for Scala and for PySpark.






We have large collection of open source products. Follow the tags from Tag Cloud >>


Open source products are scattered around the web. Please provide information about the open source projects you own / you use. Add Projects.