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type of unsupervised learning is called clustering. In this type of learning, The goal is not to maximize a utility function, but simply to find similarities in the training data.

The assumption is often that the clusters discovered will match reasonably well with an intuitive classification. For instance, clustering individuals based on demographics might result in a clustering of the wealthy in one group and the poor in another. Clustering can be useful when there is enough data to form clusters (though this turns out to be difficult at times) and especially when additional data about members of a cluster can be used to produce further results due to dependencies in the data.

In a binomial distribution, only 2 parameters, namely n and p, are needed to determine the probability. Where p is the probability of success and q is the probability of failure in a binomial trial, then the expected number of successes in n trials.

This is a binomial distribution because there are only 2 possible outcomes (we get a 5 or we don't).

Using K means clustering you can create group of objects based on their properties. Where K is number of the groups. In this case, in each group you determine the center of the group and then find the how far each object characteristics from the center. If it is near the center than it can be part of the group. Suppose we have 100 objects and we need to determine 4 groups. Hence, here K=4. Now we determine 4 center values and based on that center value we determine the distance of each object from the center.

Explanation : k-Nearest Neighbors

Pros: High accuracy insensitive to outliers, no assumptions about data

Cons: Computationally expensive, requires a lot of memory

Works with: Numeric values, nominal values

Explanation :

The difference between their properties can be promptly summarized as follows:

Table Description automatically generated

Explanation : The RMSE serves to aggregate the magnitudes of the errors in predictions for various times into a single measure of predictive power. RMSE is a good measure of accuracy, but only to compare forecasting errors of different models for a particular variable and not between variables, as it is scale-dependent.

The opposite of supervised learning is a set of tasks known as unsupervised learning. In unsupervised learning, there's no label or target value given for the data. A task where we group similar items together is known as clustering. In unsupervised learning, we may also want to find statistical values that describe the data. This is known as density estimation. Another task of unsupervised learning may be reducing the data from many features to a small number so that we can properly visualize it in two or three dimensions