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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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