Machine Learning

Unboundlocalerror local variable referenced before assignment is a common error when we are dealing with variable scope within functions. The cause of the error is referring to a local variable before it has been assigned a value. In this blog we will see the error code snippet with Unboundlocalerror local variable referenced before assignment and […]

Missing data is a common problem in data science domain. When we are collecting raw data from the environment we get lot of missing values. In Pandas missing values are typically represented as nan (Not a Number). Dropping rows with nan values helps us to maintain the integrity of our analysis by ensuring that only

Understanding pandas dataframe append deprecated. Recent deprecation of ‘DataFrame.append’ method in Pandas has created a discussion point amongst data scientists and developers. As we move to Pandas 2.0, this method is no longer recommend to be used which leads developers to use ‘pandas.concat’ for appending data. By using ‘pandas.concat’ we enhance performance in data manipulation

When we are performing data cleaning, data manipulation or data wrangling we need to extract a text between strings, characters or delimiters. In this blog post we will explore different methods to extract text between two strings in Python. We will see approaches for handling multi-line text and using regular expressions Get text between two

In Python, when we develop big projects it is difficult to write all the code in single file. The code is divided into sub files which help to modular the code. It make our debugging and understand of project structure better. One common scenario that developers encounter is, how to import Python files from the

What is the “Python Got Multiple Values for Argument” Error mean? In simple terms, the “multiple values for argument” error occurs when we pass more than one value for arguments of the function or method and Python doesn’t know which one to use. When we define a function or method we specify the arguments or

What is regularization ? Regularization is a technique in machine learning for preventing overfitting and improving model generalization. Some examples of regularization techniques are L1 Regularization (Lasso), L2 Regularization (Ridge), Elastic Net, Dropout, Early Stopping etc . Note: The coefficients represent the estimated weights for each feature in the model in context of Lasso and

What is the Bottom Layer of a Decision Tree? Decision trees are supervised algorithms used for developing classification and regression models in machine learning. The bottom layer also known as the leaf layer is where final predictions are made. After splitting the data multiple times throughout the tree, the final output is derived in the

What is the “Failed Building Wheel” Error? When we try to install a Python package some time we see error “Failed Building Wheel”. This error indicates that the installation process couldn’t compile a wheel file for that package. In this error “Error failed building wheel for numpy” it failed to compile for Numpy Package. What