How to create a smaller dataset for a specific month
Suppose we have a file that contains historical weather temperature between years 1948 to 2024 in which we want to look at how January temperatures have changed over time.
Day: January 16, 2024
Process of Fitting the models in machine learning
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 Engineering: Scaling, Normalization, and Standardization
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…
Handling missing data in a dataset
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…
Finding missing data in a dataset
Do we have a complete dataset in a real-world scenario? No. We know from history that there is missing information in our data! How can we tell if the data we have available is complete? We could print the entire dataset, but this could involve human error, and it would become impractical with this many…
Improve model with hyperparameters
Simple models with small datasets can often be fit in a single step, while larger datasets and more complex models must be fit by repeatedly using the model with training data and comparing the output with the expected label. If the prediction is accurate enough, we consider the model trained. If not, we adjust the…
Train-and-test isn’t the only approach
It’s worth keeping in mind that train-and-test is common, but not the only widely used approach in machine learning. Two of the more coming alternatives are the hold-out approach and statistical approach methods. hese statistical methods are powerful, well established, and form the foundation of modern science. The advantage is that the training set doesn’t…