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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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