This repo includes assignments for the Machine Learning-1 course at the Tech Leaders Program at Plaksha University, where the following algorithms have been used to made predictions and extract meaning out of unstructured data.
Linear Regression: Linear regression is a popular machine learning algorithm used to predict a continuous target variable based on one or more predictor variables. It works by fitting a linear equation to the data, which can then be used to make predictions. Linear regression is simple to understand and implement, making it a popular choice for many applications.
Ridge Regression: Ridge regression is a type of linear regression that is used when the data suffers from multicollinearity, which is when the predictor variables are highly correlated with each other. Ridge regression adds a penalty term to the cost function of linear regression, which helps to reduce the impact of multicollinearity on the model. This makes it a useful technique for improving the performance of linear regression in situations where multicollinearity is present.
Elastic Net: Elastic Net is a regularized regression technique that combines the features of both Ridge and Lasso regression. It is useful when there are many predictors in the dataset and some of them are highly correlated. Elastic Net combines the L1 penalty of Lasso and the L2 penalty of Ridge regression to produce a hybrid penalty function that can perform both variable selection and regularization at the same time.
Support Vector Machines (SVM): Support Vector Machines is a popular machine learning algorithm that can be used for both classification and regression problems. SVM works by finding the hyperplane that maximizes the margin between the two classes. It is a powerful algorithm that can handle non-linearly separable data by using kernel functions. SVM is widely used in image classification, bioinformatics, and text classification.
Decision Trees: Decision Trees are a popular machine learning algorithm that can be used for both classification and regression problems. They work by recursively partitioning the data into smaller and smaller subsets based on the values of the predictor variables. Decision Trees are easy to understand and interpret, making them a popular choice for many applications.
K-Means Clustering: K-Means Clustering is an unsupervised learning algorithm that is used for clustering similar data points together. It works by partitioning the data into k clusters, where k is the number of clusters specified by the user. K-Means Clustering is widely used in image segmentation, customer segmentation, and anomaly detection.