This article explains various Machine Learning model evaluation and validation metrics used for classification models. Continue reading “Machine Learning Model Evaluation and Validation”
Use Docker to package your Python Flask app and your Conda environment. This post will describe how to dockerize your Python Flask app and recreate your Conda Python environment. Continue reading “Dockerizing Python Flask app and Conda environment”
Get your Python code for data preparation to perform significantly faster with just a few lines of code. Take advantage of the build in Concurrent futures
This post will discuss and show how to utilize all your CPU cores when executing your Python code for data preparation by just adding a few lines of extra code.
Continue reading “Optimize data preparation code using Python concurrent futures”
In this article, I will explain and show how I use Python with Anaconda and PyCharm to set up a python data science environment ready for local experimentation with the most popular Python libraries for Machine Learning / Data Science.
This article is focused on Mac users, however, don’t panic, I will make short comments on how to achieve the same results on Windows. I myself use both so no preference there.
Continue reading “How to setup a Python Data Science environment – Setting up Anaconda environments for working on data science problems using Python”
This is the first article in a series of articles about working with stock price data and implementing the up-trendline indicator with Python. The complete series will describe in detail implementation of the technical indicator called up-trendline. This article will describe the part of the solution which consumes the REST API, which will give us the data we will need in subsequent articles. Continue reading “Implementing the up-trendline indicator with Python — From acquiring data to modeling an algorithm and implementing a solution — Part 1 consuming a REST API”