Linear regression A lgorithm Concept of Linear regression In order to model the relationship between a dependent variable and one or more independent variables, linear regression is a machine learning algorithm. The goal of linear regression is to find a linear equation that best describes the relationship between the variables. Using the values of the independent variables as a starting point, this equation can then be used to predict the value of the dependent variable. There is simply one independent variable and one dependent variable in basic linear regression. The linear equation takes the form of y = mx + b, where y is the dependent variable, x is the independent variable, m is the slope of the line, and b is the y-intercept. For example, let's say we have a dataset of the number of hours studied and the corresponding test scores of a group of students. We can use linear regression to find the relationship between the two variables and predict a student's test scor

Machine Learning Algorithms with Python Code Contents of Algorithms  1.  ML Linear regression A statistical analysis technique known as "linear regression" is used to simulate the relationship between a dependent variable and one or more independent variables. 2.  ML Logistic regression  Logistic regression: A statistical method used to analyse a dataset in which there are one or more independent variables that determine an outcome. It is used to model the probability of a certain outcome, typically binary (yes/no). 3.  ML Decision trees Decision trees: A machine learning technique that uses a tree-like model of decisions and their possible consequences. It is used for classification and regression analysis, where the goal is to predict the value of a dependent variable based on the values of several independent variables. 4.  ML Random forests Random forests: A machine learning technique that uses multiple decision trees to improve the accuracy of predictions. It creates a f

Ensemble method Algorithms  Concepts of Ensemble methods E nsemble methods - bagging, boosting, and stacking Ensemble methods are a type of machine learning technique that combines multiple individual models to improve the overall performance of the system. There are several ensemble methods, but three of the most commonly used ones are: Bagging (Bootstrap Aggregating) : Bagging is a technique that uses multiple independent models in parallel to make a prediction. Each model is trained on a subset of the training data, which is randomly sampled with replacement. The final prediction is made by averaging the predictions of all the individual models. Boosting : Boosting is a technique that uses multiple models sequentially. Each model is trained on the entire training set, but the weights of the training examples are adjusted based on the performance of the previous model. The final prediction is made by combining the predictions of all the individual models. Stacking : Stacking i

Unsupervised Learning Algorithm -  Hierarchical Clustering Hierarchical clustering Concepts Hierarchical clustering is a popular unsupervised machine learning algorithm used to cluster or group similar data points together in a dataset. Hierarchical clustering does not need the user to predetermine the number of clusters, in contrast to K-Means clustering. The algorithm works by creating a hierarchy of clusters, where each data point initially forms its own cluster, and clusters are successively merged based on their similarity. Here is an example of how Hierarchical clustering works : Suppose we have a dataset of customer transactions, where each transaction includes the customer's age, income, and spending behaviour. We want to group customers with similar spending behaviour together for targeted marketing campaigns. We use Hierarchical clustering to create a hierarchy of clusters based on the similarity of their spending behaviour. The algorithm initially assigns each customer