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Regression  is  a  mathematical  approach  to  find  a  relationship  between  two  or  more  variables.  It  works  with
              continuous data. This can be used for weather forecasting, time series modelling, etc. In order to get the best fit
              results, the distance between the line and data points should be minimum.
              Let us see some examples of the Regression Model:
                 • Income Prediction: Predict a person's annual income based on demographics. The model is trained with input
                features like age, education, hours per week, it can take value within a specific range. The output is a continuous
                value, annual income of a person.
                 • House Price Prediction: The model predicts the selling price of a house based on the input features like size of
                the house, number of rooms, location, market price of the house, etc. The model predicts the price of the house
                based on features of the house.
                 • Temperature Prediction: Temperature is a continuous variable, capable of taking any value within a given
                range. Regression models are ideal for predicting such continuous outcomes. This model estimates temperature
                based on input features such as humidity, wind speed, cloud cover, atmospheric pressure, and prior temperature
                readings.
                 • Car Price Prediction: This model estimates the selling price of a car using various factors, like fuel type, years
                of usage, number of previous owners, distance driven (in kilometers), transmission type (manual or automatic).
              Since the model predicts a continuous value, i.e., the approximate price of the car based on the input data, it falls
              under the category of regression models.
              Note, Continuous data includes values that can be measured and take any value within a range (e.g., height,
              temperature). It is analysed using regression and probability distributions.
              Discrete data consists of countable values with no in-between (e.g., number of students, dice rolls). It is analysed
              using frequency counts and probability tables.

              Sub Categories of Unsupervised Learning

              Unsupervised Learning can further be divided into: Clustering and Association. Let us discuss these in detail.
              Clustering

                                                                                                   short hair people
              Clustering is a machine learning approach where the machine
                                                                                                   long hair people
              partitions  the  dataset  into  different  clusters  or  categories  based
              on machine generated algorithms. The data fed to such a model
              is usually unlabelled or random and thus the developer feeds in
              the data directly into the machine and instructs it to build its own
              algorithm. The machine then forms a pattern or cluster based on
              training data and groups those that follow the same pattern. Like,
              Model segregates people with long and short hair and forms two
              clusters based on it as shown in the graph.
              The best clustering is the one that minimises the error. Clustering works on discrete dataset. For example, if you
              have random data of insects and reptiles, since you are unable to find any meaningful pattern amongst them, you
              would feed their data into the clustering algorithm. The algorithm would then analyse the data and divide them
              into clusters according to their similarities based on the trends noticed. The clusters are then given as the output.


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