PCA (Principal Component Analysis) in Python using Scikit-learn is a technique used to reduce the number of features in a dataset while preserving most of the variance (information). It works by: Finding new axes (principal components) that capture the most variance. Projecting the data onto these fewer dimensions. It’s useful for visualization, speeding up models, […]
Reflexion Prompting
This technique is highly effective for chatbots and problem-solving tasks. It also helps reduce hallucinations by incorporating a form of quality control. The process involves: Starting with an initial prompt Getting the AI’s first response Sending a reflexion prompt asking the AI to review and reflect on its first answer Receiving an optimized response, improved […]
Python Pandas Lambda Function
Lambda functions in Python are small, anonymous functions defined using the lambda keyword. They are typically used for short, throwaway functions that are needed for a brief period, such as within map(), filter(), or sorted() calls. A lambda can take any number of arguments but only one expression, which is evaluated and returned. For example, […]
Simple Imputer
When working with data in Python, especially using pandas, handling missing values is a crucial step in data cleaning. Missing values can occur in both categorical and numeric columns. There are several common strategies to address them: you can choose to ignore them (though this is rarely recommended), remove the rows that contain them using […]
Logistic Regression
Logistic regression is a statistical model used for binary classification problems, where the goal is to predict one of two possible outcomes. Unlike linear regression, which predicts continuous values, logistic regression estimates the probability that a given input belongs to a particular class. It uses the logistic (sigmoid) function to map predicted values between 0 […]