Applied Data Science Coding with Python: Regression with KNN Algorithm Regression with the K-Nearest Neighbors (KNN) algorithm is a method for solving regression problems in machine learning. It is based on the idea that similar data points tend to have similar target variable values. The KNN algorithm for regression starts by finding the K number …
Applied Data Science Coding with Python: Regression with ElasticNet Algorithm Regression with the ElasticNet algorithm is a method for solving regression problems in machine learning. It is a linear regression model that combines both L1 and L2 regularization. The ElasticNet algorithm starts by defining a linear model with a combination of L1 and L2 regularization. …
Applied Data Science Coding with Python: Regression with CART Algorithm Regression with the Classification and Regression Tree (CART) algorithm is a method for solving regression problems in machine learning. It is used to create a decision tree that can be used to make predictions based on the input data. The CART algorithm starts by recursively …
Applied Data Science Coding with Python: SVM Algorithm The Support Vector Machine (SVM) algorithm is a method for classification and regression in machine learning. It is used to find the best boundary (or hyperplane) that separates different classes in the dataset with the greatest possible margin. The SVM algorithm starts by mapping the input data …
Applied Data Science Coding with Python: Naive Bayes Algorithm The Naive Bayes algorithm is a method for classification in machine learning. It is based on Bayes’ theorem which states that the probability of an event occurring is equal to the prior probability of the event multiplied by the likelihood of the event given certain evidence. …
Applied Data Science Coding with Python: LR Algorithm The Logistic Regression (LR) algorithm is a method for classification in machine learning. It is used to model the probability of a certain class or event occurring, given the values of certain features or inputs. The LR algorithm starts by finding the best set of coefficients that …
Applied Data Science Coding with Python: LDA Algorithm The Linear Discriminant Analysis (LDA) algorithm is a method for classification in machine learning. It is used to find a linear combination of features that separates different classes in the dataset with the greatest possible margin. The LDA algorithm starts by finding the mean vectors of the …
Applied Data Science Coding with Python: KNN Algorithm The K-Nearest Neighbors (KNN) algorithm is a method for classification and regression in machine learning. It is based on the idea that similar data points tend to have similar outcomes or labels. The KNN algorithm works by finding the K number of data points in the training …
Applied Data Science Coding with Python: CART Algorithm The CART (Classification and Regression Tree) algorithm is a powerful tool for both classification and regression problems in machine learning. It is used to create a decision tree that can be used to make predictions based on the input data. The CART algorithm works by recursively splitting …
Applied Data Science Coding in Python: How to standardize Data Standardizing data in Python using scikit-learn is a way to bring all of the features in your dataset to the same scale. This is useful because some machine learning models can be sensitive to the scale of the input features. The process of standardizing involves …