Derek Kweku's repositories
26-Weeks-Of-Data-Science
Email Newsletter
ML-For-Beginners
12 weeks, 25 lessons, 50 quizzes, classic Machine Learning for all
Advanced-Lane-Lines
A pipeline that can detects lane boundaries, predicts upcoming curves, and measures lane curvature.
amber-methodology
How to use machine learning to find interesting places on satellite maps
c9-python-getting-started
Sample code for Channel 9 Python for Beginners course
Cloud-Net-A-semantic-segmentation-CNN-for-cloud-detection
A semantic segmentation CNN for cloud detection
COVID-19
Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE
Customer-Survival-Analysis-and-Churn-Prediction
In this project, I have utilized survival analysis models to see how the likelihood of the customer churn changes over time and to calculate customer LTV. I have also implemented the Random Forest model to predict if a customer is going to churn and deployed a model using the flask web app.
Customer-Survival-Analysis-and-Churn-Prediction-for-an-Internet-Service-Provider
Customer Survival Analysis and Churn Prediction App: https://churn-prediction-app.herokuapp.com/ Customer attrition, also known as customer churn, customer turnover, or customer defection, is the loss of clients or customers. Telephone service companies, Internet service providers, pay TV companies, insurance firms, and alarm monitoring services, often use customer attrition analysis and customer attrition rates as one of their key business metrics because the cost of retaining an existing customer is far less than acquiring a new one. Companies from these sectors often have customer service branches which attempt to win back defecting clients, because recovered long-term customers can be worth much more to a company than newly recruited clients. Predictive analytics use churn prediction models that predict customer churn by assessing their propensity of risk to churn. Since these models generate a small prioritized list of potential defectors, they are effective at focusing customer retention marketing programs on the subset of the customer base who are most vulnerable to churn. In this project I aim to perform customer survival analysis and build a model which can predict customer churn. I also aim to build an app which can be used to understand why a specific customer would stop the service and to know his/her expected lifetime value.
Data-science
Collection of useful data science topics along with code and articles
deepLearningBook-Notes
Notes on the Deep Learning book from Ian Goodfellow, Yoshua Bengio and Aaron Courville (2016)
faker
Faker is a Python package that generates fake data for you.
fastbook
Draft of the fastai book
HashingDeepLearning
Codebase for "SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems"
introduction_to_ml_with_python
Notebooks and code for the book "Introduction to Machine Learning with Python"
jupyter-matplotlib
Matplotlib Jupyter Extension
materials
Bonus materials, exercises, and example projects for our Python tutorials
ML2021-Spring
**Official** 李宏毅 (Hung-yi Lee) 機器學習 Machine Learning 2021 Spring
ML_from_scratch
This repo contains machine learning algorithms implemented from scratch
mslearn-aml-labs
Azure Machine Learning Lab Notebooks
post-tuto-deployment
Build and deploy a machine learning app from scratch 🚀
pyrobolearn
PyRoboLearn: a Python framework for Robot Learning
Stocks
Programs for stock prediction and evaluation
wordcloud
Tutorial on using Python Wordcloud