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• Weather Classification: In this scenario, the model is trained with features like temperatures, humidity, wind
                speed and pressure to predict the type of weather. The trained model is now capable of predicting the weather
                based on the atmospheric conditions under the category of “Sunny”, “Rainy”, ”Cloudy”.









                                                                            Classification
                                                                               Model






                                          Input                                                       Output


              Regression


              Regression algorithms predict a continuous value based on the input
              variables. It is an example of a rule-based AI model. In regression,
              the algorithm generates  a mapping function from the data,  as
              shown by the solid line in the given graph. The green dots shown in
              the graph are the data values and the solid line here represents the
              mapping done for them. With the help of this mapping function, we
              can predict the future data. For example, if we want to predict the
              temperature of a day in a year, we can use past year’s temperature
              for that day as training data and can predict it for the coming year.
              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.


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