Blog Posts

Ridge Regression is a type of linear regression that addresses multicollinearity (when independent variables are highly correlated) by applying a regularization technique. It is used when the data suffers from overfitting, meaning the model performs well on the training data but poorly on new, unseen data. How Ridge Regression Works: In ordinary linear regression, the

Statistical modeling is the process of using mathematical frameworks to represent and analyze real-world data and phenomena. It involves creating a mathematical description or model that captures the relationships between variables in a dataset, helping to make predictions, test hypotheses, or infer conclusions about the data. The goal of statistical modeling is to understand underlying

Exploratory Data Analysis (EDA) is a critical step in the data science process where a data scientist analyzes and investigates datasets to summarize their main characteristics, often using visual methods. It helps to understand the underlying structure of the data, detect patterns, spot anomalies, and check assumptions before applying any formal modeling techniques. EDA is