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This question is so easy: agile approach. Why? Because it recognizes that the construction of the solution requires different loops. Reason 1: ML models change overtime Machine Learning projects are supported on ML models, and models change overtime. Why do a model change overtime? A model change overtime because the data used to train the … Read more
These are some notes about the basis of Sagemaker Sagemaker services SageMaker Neo optimizes the trained model and compiles it into an executable. Taking the target hardware where the model will be run as input; the compiler uses a ML model to apply performance optimizations on your model. Ground truth makes easy to label data. … Read more
The cross-industry standard process for data mining or CRISP-DM is an open standard process framework model for data mining project planning, created in 1996. The process of CRISP-DM is into 6 phases or components: Business understanding – What does the business need? Data understanding – What data do we have / need? Is it clean? Data preparation – How do we organize … Read more
This code is the result of some work I have done on LSTM. Purpose: predict the next day price with the use of a LSTM neural network and just one property. How: I have normalized the data, I have used just 1 parameter as input (close price), I have built the code using basic functions … Read more
During year 4 – Q4 I have focused on the writing of books, and I still have to finish the English version of the “Cuaderno de trabajo para dibujar estrategias“. I will focus on Wardley Maps and completing my PMP renewal (I have 55 PDUs to complete). The plan So the V2MOM for this quarter will be: Vision: Work … Read more
These are reminder notes about Box-cox transformation. One of the problems that box-cox transformation tries to solve is “heteroscedasticity” (non-constant variance). This article explains the problem where you can apply box-cox transformation to solve it: https://blog.minitab.com/en/applying-statistics-in-quality-projects/how-could-you-benefit-from-a-box-cox-transformation SciPy has added an inverse Box-Cox transformation: https://docs.scipy.org/doc/scipy/reference/generated/scipy.special.inv_boxcox.html Does Box-cox always work? The answer is NO. Box-cox does not … Read more
I have done this course proposed by Kaggle, and I would like to take some notes. The trend component of a time series represents a persistent, long-term change in the mean of the series. We mainly have: Time dependent properties: trends and seasonality. Serial dependent properties: Cycles and lagged series 1. Trends The trend component of a time series … Read more
Some notes of this course offered by Kaggle, for my poor memory. Cross validation Cross-validation gives a more accurate measure of model quality, which is especially important if you are making a lot of modeling decisions. Use pipelines for doing cross-validation, you will save a lot of time. XGBoost = Gradient boosting We refer to … Read more
If you have a mine, the cost of obtaining a kilo of gold, aluminum, cooper or whatever, they all measure the cost of obtaining a kilo of the target element. With the machine learning projects, the situation is similar, and the definition of cost of data exploitation is something that should be defined from early … Read more
This post gather the analysis of data done with market breadth data, VIX, DIX and GEX. They are moved into a Long Short Term Memory (LSTM) neural network, that is a recurrent neural network. You can check the code and read how the whole thing is done. Input data The used features as input are: … Read more