In this article, we will discuss top 6 machine learning algorithms for classification problems, including: logistic regression, decision tree, random forest, support vector machine, k nearest neighbour and naive bayes. The best example of an ML classification algorithm is Email Spam Detector. The main goal of the Classification algorithm is to identify the category of a given dataset, and these algorithms are mainly used to predict the output for the categorical data.

1. DIFFERENT ALGORITHMS
USED IN
CLASSIFICATION

2. WHAT IS CLASSIFICATION
• Classification is used to identify the category of new observation on the basis of
training data
• Here the model learns from the given dataset and then classifies the new data
into a number of classes.
• Classification can be of two types:
• Binary Classification
• Multi-Class Classification

3. HOW CLASSIFICATION WORKS
• Classification is performed in following manner:
• At first, the training dataset(Labelled) is given to the Classifier (Classification
Algorithm).
• The Classifier analyses the dataset provided and recognizes the patter present in the
dataset.
• After that a suitable Classification model is chosen according to the task to be
performed.
• At last, the testing dataset (Labelled) is provided to model for classification to check
the performance of the model.

4. CLASSIFICATION ALGORITHMS
• Classification Algorithms are classified into two types
• Linear: Logistic Regression, Support Vector Machine (SVM)
• Non-Linear: K-nearest Neighbours(KNN), Kernel SVM, Decision Tree
• This division of Algorithms into Linear and Non-Linear is done on the basis of
dataset (Linear Separable or Non-Linearly Separable)

5. LOGISTIC REGRESSION
• This works on Categorical and Linearly Separable data.
• Goes best with Binary Classification.
• Uses Sigmoid Function (y=1/1+e^-x) for classification of data.
• Sigmoid Function: The Sigmoid function coverts the independent variable
into a expression of probability that ranges between [0,1] wrt to dependent
variable.
• The threshold value is considered as 0.5
• If the probability of the variable is >0.5 then it will be considered as 1 or that it
belongs to class A (upper class).
• If the probability of the variable is >0.5 then it will be considered as 0 or that it
belongs to class B (lower class)

6. SUPPORT VECTOR MACHINE (SVM)
• Works on Linearly Separable data.
• Classifies the data into different categories with the help of Decision Boundary.
• Decision Boundary (Hyperplane) is drawn in the graph(drawn by the analysis of
dataset) in such a manner that it divides the datapoints into two categories, in
order to decide the new dataset will belong to which class.
• The Margin for the hyperplane is calculated(which plays an important role in
deciding the hyperplane.
• Margin is the distance between the two Support Vectors.
• The points which were considered nearest to the hyperplane from both classes
and from which the line parallel to hyperplane is drawn are known as Support
Vectors.
• The hyperplane with maximum margin will be considered as the final hyperplane
(Maximal Margin Hyperplane – MMH)

7. K-NEAREST NEIGHBOURS (KNN)
• Works on Non-Linearly Separable data.
• K in KNN refers to the number of neighbours that should be considered.
• K number of datapoints with minimum distance from the datapoint to be
classified are considered.
• Euclidian Distance is used to calculate the distance between two datapoints
• Euclidian Distance d= (((|Xo1-Xa1|^2)+(|Xo2-Xa2|^2))^1/2)
• The classification is done on the basis of probability with respect to the classes
of those K neighbours.
• In the case of 50% probability, one more neighbour is taken into consideration.

8. KERNEL SUPPORT VECTOR MACHINE
• Works on Non-Linearly Separable data.
• Kernel SVM works with the help of Kernel function.
• Kernel Function is used to convert the low dimension
feature space to high dimensional feature space.
• The conversion of data from LDFS to HDFS helps to
draw the hyperplane in order to classify the data.

9. DECISION TREE
• Works on Non-Linearly Separable dataset.
• Tree Structured
• Contains two types of nodes:
• Decision Nodes
• Leaf Nodes
• Root Node is provided with the whole dataset from
where data splitting is done according to the
situation.

10. DIFFERENT ALGORITHMS
USED IN CLUSTERING

11. CLUSTERING
• Clustering is the task of dividing the population or data points into a number of groups
such that data points in the same groups are more similar to other data points in the
same group and dissimilar to the data points in other groups. It is basically a collection of
objects on the basis of similarity and dissimilarity between them.

12. WHAT ARE THE USES OF CLUSTERING?
Clustering has a myriad of uses in a variety of industries. Some common applications for clustering
include the following:
• Market Segmentation
•Statistical data analysis
•Social network analysis
•Image segmentation
•Anomaly detection, etc.

13. TYPES OF CLUSTERING METHODS
1.Partitioning Clustering
2.Density-Based Clustering
3.Distribution Model-Based Clustering
4.Hierarchical Clustering

14. PARTITIONING CLUSTERING
• It is a type of clustering that divides the data into non-hierarchical groups. It is
also known as the centroid-based method.
• The most common example of partitioning clustering is the K-Means Clustering
algorithm.

15. K- MEANS CLUSTERING ALGORITHM
• K-Means Clustering is an Unsupervised Learning algorithm, which
groups the unlabeled dataset into different clusters.
• It is an iterative algorithm that divides the unlabeled dataset into k
different clusters in such a way that each dataset belongs only one
group that has similar properties.
• It allows us to cluster the data into different groups and a
convenient way to discover the categories of groups in the
unlabeled dataset on its own without the need for any training.

16. DENSITY BASED CLUSTERING
• identify distinctive clusters in the data
• based on the idea that a cluster/group in a data space is a contiguous
region of high point density
• separated from other clusters by sparse regions

17. HIERARCHICAL CLUSTERING