Applied Statistics

Compare Machine Learning Algorithms with IRIS Dataset

Compare Machine Learning Algorithms with IRIS Dataset Comparing machine learning algorithms with the IRIS dataset in Python is a common task in machine learning, as it allows to evaluate the performance of different algorithms on a known dataset and choose the best one for a specific problem. The IRIS dataset is a popular dataset for …

Applied Data Science Coding with Python: Regression with Support Vector Machine Algorithm

Applied Data Science Coding with Python: Regression with Support Vector Machine Algorithm Regression with the Support Vector Machine (SVM) algorithm is a method for solving regression problems in machine learning. It is a type of supervised learning algorithm that can be used for both linear and non-linear regression. The SVM algorithm for regression starts by …

Regression with Ridge Algorithm

Regression with Ridge Algorithm Regression with the Ridge algorithm is a method for solving regression problems in machine learning. It is a linear regression model that includes L2 regularization, which is a technique that adds a penalty term to the loss function to reduce the complexity of the model. The Ridge algorithm starts by defining …

Applied Data Science Coding with Python: Linear Regression Algorithm

Applied Data Science Coding with Python: Linear Regression Algorithm Linear Regression is a statistical method for predicting a continuous variable from one or more variables. Linear regression is one of the simplest and most widely used predictive models in machine learning. It assumes that the relationship between the independent variables and the dependent variable is …

Applied Data Science Coding in Python: How to rescale Data

Applied Data Science Coding in Python: How to rescale Data Rescaling data is a technique used to transform the values of a dataset to be in a specific range. This is often done to make sure that data is on the same scale before applying machine learning algorithms. There are different ways to rescale data, …

Applied Data Science Coding in Python: How to do Binarization

Applied Data Science Coding in Python: How to do Binarization Binarization is the process of converting a continuous or numeric variable into a binary variable. The binary variable can take on only two values, for example, 0 and 1, true and false, or yes and no. This process is often used in machine learning and …

Applied Data Science Coding in Python: scatter plots

Applied Data Science Coding in Python: scatter plots A scatter plot is a graphical representation of two-dimensional data, where each point on the plot represents a pair of (x,y) values. It is used to visualize the relationship between two continuous variables. Scatter plots can be used to identify patterns in the data, such as linear …

Applied Data Science Coding in Python: histogram plots

Applied Data Science Coding in Python: histogram plots A histogram is a graphical representation of the distribution of a dataset. It is an estimate of the probability distribution of a continuous variable. In other words, it shows how often certain values appear in a dataset. The histogram groups the values into bins, and the height …

Applied Data Science Coding in Python: How to generate density plots

Applied Data Science Coding in Python: How to generate density plots Density plots, also known as probability density plots, are used to visualize the probability density function of a continuous random variable. It gives an idea of the distribution of the data and helps to identify patterns, such as skewness or outliers. In Python, there …

Applied Data Science Coding in Python: How to generate Correlation Matrix

Applied Data Science Coding in Python: How to generate Correlation Matrix A correlation matrix is a table that shows the correlation coefficients between multiple variables. It is a useful tool for understanding the relationship between different variables in a dataset. Correlation coefficient can range from -1 to 1, indicating the strength and direction of the …