eaborn is a data visualization library for Python that is built on top of Matplotlib. It provides a high-level interface for creating attractive and informative statistical graphics. Seaborn is particularly well-suited for visualizing complex datasets with multiple variables. Key features of Seaborn include: To use a library in your Python code, you typically need to…
andas is a powerful open-source data manipulation and analysis library for Python. It provides data structures for efficiently storing, manipulating, and analyzing structured data, such as tabular data and time series. Key features of Pandas include: To use Pandas, you typically start by importing it into your Python script or Jupyter Notebook: After importing, you…
In Google Colab, you can use np.save to save NumPy arrays to your Google Drive. Here are the steps: Mount Google Drive Start by mounting your Google Drive. Run the following code and follow the instructions to authorize and mount your Google Drive:
hen you create a subset of a NumPy array and modify its values, it can affect the original array if the subset is actually a view of the original array rather than a copy. NumPy provides views to enhance performance and memory efficiency by avoiding unnecessary data copying. Understanding whether you’re working with a view…
umPy is a powerful numerical library in Python that provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these elements. It is a fundamental package for scientific computing in Python and is widely used in various domains such as data science, machine learning, signal processing, and…
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.
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…
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…
