Classification Algorithm Basic | Data Science
Classification is a type of supervised learning in data science that involves predicting a categorical target variable based on one or more input variables.
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Here are some popular classification algorithms along with a real time example.
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Logistic regression.
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Logistic regression is a binary classification algorithm that is used to predict the probability of an event occurring.
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It works by fitting A sigmoid function to the input data, which maps the input features to the target variable.
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Real time example.
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Logistic regression can be used in credit scoring to predict whether a loan applicant is likely to default on their loan based on their credit history and other financial factors.
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Decision trees.
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Decision trees are a popular classification algorithm that work by splitting the data into different branches based on the most important features.
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They use a tree like structure to represent the decision making process and can handle both categorical and continuous input variables.
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Real time example.
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Decision trees can be used in customer segmentation to predict the likelihood of a customer buying a particular product based on their demographic and behavioral data.
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Random Forest.
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Random Forest is an ensemble learning method that combines multiple decision trees to improve the accuracy and generalization of the model.
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It works by constructing a set of decision trees on different subsets of the input data and then combining their predictions.
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Real time example.
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Random Forest can be used in fraud detection to predict whether a particular transaction is fraudulent based on a variety of input features such as transaction amount, location, and time.
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Support vector machines, SVMS.
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VMS are a powerful classification algorithm that works by finding the hyperplane that best separates the input data into different classes.
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They are particularly effective in cases where the input data is not linearly separable.
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Real time example.
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SBMS can be used in image classification to predict whether an image contains a particular object or not based on its visual features.
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These classification algorithms are widely used in data science for a variety of applications, such as predicting customer behavior, fraud detection, and image recognition.
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