n scikit-learn (sklearn), the StandardScaler is a preprocessing technique used to standardize features by removing the mean and scaling them to have a unit variance. Standardization is a common step in many machine learning algorithms, especially those that involve distance-based calculations or optimization processes, as it helps ensure that all features contribute equally to the…
One-hot encoding is a technique used in machine learning and data preprocessing to represent categorical variables as binary vectors. In one-hot encoding, each category or label in a categorical variable is represented as a binary vector, where each element corresponds to a unique category. The process involves the following steps: For example, consider a dataset…
In pandas, you can give a specific sorting order to categorical values by creating a categorical variable with an ordered category. Here’s an example: In this example: This can be useful when you want to ensure that certain operations, such as sorting or plotting, take into account the natural order of the days of the…
To cap or clip outliers in a column, you can use the clip method in pandas. The clip method allows you to set a minimum and maximum threshold for the values in a DataFrame or a specific column. Here’s an example: Clipping is a simple method, and it’s important to consider the impact on your…
hen dealing with missing data of type categorical, several methods can be used to impute the missing values. Here are some common approaches: The choice of imputation method depends on the nature of the data, the underlying patterns, and the goals of the analysis. Always consider the context of the data and the potential impact…
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…
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…
