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Classification is the process of predicting the class of given data points. Classes are sometimes called as targets/ labels or categories.

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Name		Name	Last commit message	Last commit date
Latest commit History 90 Commits
01 Introduction of Classification		01 Introduction of Classification
02 Types of Classification Algorithm		02 Types of Classification Algorithm
03 Classification Algorithms		03 Classification Algorithms
04 Exploratory Data Analysis		04 Exploratory Data Analysis
05 Decision Tree		05 Decision Tree
06 Exploratory Data Analysis		06 Exploratory Data Analysis
07 K-Nearest Neighbors		07 K-Nearest Neighbors
08 Logistic Regression Classifier		08 Logistic Regression Classifier
09 Naive_Bayes classifier		09 Naive_Bayes classifier
10 Random Forest		10 Random Forest
11 Stochastic Gradient Descent		11 Stochastic Gradient Descent
12 Support Vector Machine		12 Support Vector Machine
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md

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Machine-Learning-Classification

Classification Process

Classification is the process of predicting the class of given data points. Classes are sometimes called as targets/ labels or categories. Classification predictive modeling is the task of approximating a mapping function (f) from input variables (X) to discrete output variables (y).

For example, spam detection in email service providers can be identified as a classification problem. This is s binary classification since there are only 2 classes as spam and not spam. A classifier utilizes some training data to understand how given input variables relate to the class. In this case, known spam and non-spam emails have to be used as the training data. When the classifier is trained accurately, it can be used to detect an unknown email.

Classification belongs to the category of supervised learning where the targets also provided with the input data. There are many applications in classification in many domains such as in credit approval, medical diagnosis, target marketing etc.