Supervised learning involves training a model on labeled data, so that it can learn to predict the correct labels for new data. Unsupervised learning involves training a model on unlabeled data , so that it can learn to find patterns in the data. Reinforcement learning involves training a model by providing it with feedback on its performance, so that it can learn to maximize its reward.
K-means clustering is a simple and effective way to cluster data points into two groups. This method is especially useful when the data set is not linearly separable.
The dataset contains customer purchase histories. The task is to cluster the customers into groups based on their purchase patterns.
A machine learning algorithm is used to identify which features in a dataset are most predictive of the target variable. This can be used to reduce the dimensionality of the data and improve the performance of the machine learning models.
Given a set of data points, this problem deals with determining which points are clustered together and which points are outliers. Outliers are points that are not close to other points in the data set.
This problem involves clustering customer reviews in order to group together similar reviews and distinguish different groups of reviews. This can be done using unsupervised learning methods such as k-means clust ering.