nivariate exploration refers to the analysis of a single variable in isolation. In data analysis, univariate exploration involves examining the distribution, central tendency, and variability of a single variable without considering its relationship with other variables. Common techniques used in univariate exploration include: Univariate exploration is often the first step in data analysis, providing insights…
The steps to follow to use machine learning models are: In “fit” and “predict” steps, you can use several models, and evaluate them, to keep the most performing one. Python libraries: Here, we train a model to guess a comfortable boot size for a dog, based on the size of the harness that fits them:…
Feature scaling is considered a part of the data processing cycle that cannot be skipped, so that we can achieve stable and fast training of our ML algorithm. eature Scaling is a technique to standardize the independent features present in the data in a fixed range. It is performed during the data pre-processing to handle…
There are many ways to address missing data, each with pros and cons. Let’s take a look at the less complex options: Option 1: Delete data with missing rows. When we have a model that cannot handle missing data, the most prudent thing to do is to remove rows that have information missing. Let’s remove…
The workflow for implementing Artificial Intelligence and Machine Learning solutions typically involves several stages. Collaborative efforts among data scientists, domain experts, and stakeholders are crucial throughout the process. The specific details of the workflow can vary based on the complexity of the problem, the type of algorithm used, and the specific requirements of the project.…
