In data science, regression is a predictive modeling technique used to estimate or predict a numerical value or continuous variable based on the relationship between a set of independent variables (also known as features or predictors) and a dependent variable (also known as the target or response variable). Regression analysis helps identify and quantify the relationship between variables, allowing us to make predictions or estimate the value of the dependent variable when the independent variables are known.
The goal of regression is to find a mathematical function or model that best fits the data and can be used to predict or estimate the target variable for new, unseen data points. This involves identifying the pattern or trend within the data and formulating an equation that represents the relationship between the independent variables and the target variable. The resulting model can then be used to make predictions or estimate the target variable for new observations.
Evaluation of the regression model involves assessing its performance and accuracy by comparing the predicted values with the actual values of the target variable. Various evaluation metrics, such as mean squared error (MSE), root mean squared error (RMSE), or coefficient of determination (R-squared), can be used to measure the goodness of fit and assess the predictive capabilities of the model. By obtaining Data Science Training, you can advance your career in Ethical Hacking. With this course, you can demonstrate your expertise in the basics of machine learning models, analyzing data using Python, making data-driven decisions, and more, making you a Certified Ethical Hacker (CEH), many more fundamental concepts, and many more critical concepts among others.
There are several types of regression techniques, including:
Simple Linear Regression: This technique assumes a linear relationship between a single independent variable and the target variable. The model finds the best-fit line that minimizes the difference between the predicted and actual values.
Multiple Linear Regression: In this technique, multiple independent variables are considered to predict the target variable. The model finds the best-fit hyperplane that minimizes the error between the predicted and actual values.
Polynomial Regression: Polynomial regression extends the linear regression model by including polynomial terms (e.g., quadratic, cubic) of the independent variables. This allows for capturing non-linear relationships between the variables.
Logistic Regression: Although named regression, logistic regression is used for classification tasks. It models the relationship between the independent variables and the probability of a binary outcome.
Ridge Regression and Lasso Regression: These are regularization techniques used to handle multicollinearity (high correlation among independent variables) and prevent overfitting in regression models.
The regression model is trained using a dataset where both the independent variables and the corresponding target variable are known. During training, the model estimates the coefficients or weights for each independent variable, which determine the impact of each variable on the target variable. The model aims to minimize the difference between the predicted values and the actual values in the training data.
Once the model is trained, it can be used to predict or estimate the target variable for new, unseen data points by inputting the values of the independent variables into the model equation. The model leverages the learned relationships to generate predictions based on the provided input.
Regression analysis is widely used in data science and offers valuable insights into the relationship between variables, allowing for accurate predictions or estimations of numerical values. It finds applications in various domains, including finance, economics, healthcare, marketing, and social sciences.
