Loan Status Prediction

• In this project we will be predicting the loan status, I have used support vector machine concept.
• This is the algorithm which is mostly used in supervised learning algorithms and also regression models.

Support Vector Machine

“Support Vector Machine” (SVM) is a supervised machine learning algorithm that can be used for both classification or regression challenges.

However, it is mostly used in classification problems.

In the SVM algorithm, we plot each data item as a point in n-dimensional space (where n is a number of features you have) with the value of each feature being the value of a particular coordinate.

Then, we perform classification by finding the hyper-plane that differentiates the two classes very well

Support Vectors are simply the coordinates of individual observation. The SVM classifier is a frontier that best segregates the two classes (hyper-plane/ line).

Types of SVM

SVM can be of two types:

Linear SVM: Linear SVM is used for linearly separable data, which means if a dataset can be classified into two classes by using a single straight line, then such data is termed as linearly separable data, and classifier is used called as Linear SVM classifier.

Non-linear SVM: Non-Linear SVM is used for non-linearly separated data, which means if a dataset cannot be classified by using a straight line, then such data is termed as non-linear data and classifier used is called as Non-linear SVM classifier.

Advantages of SVM :

• SVM works relatively well when there is a clear margin of separation between classes.

• SVM is more effective in high dimensional spaces.

• SVM is effective in cases where the number of dimensions is greater than the number of samples.

• SVM is relatively memory efficient

Disadvantages of SVM :

• SVM algorithm is not suitable for large data sets.
• SVM does not perform very well when the data set has more noise i.e. target classes are overlapping.
• In cases where the number of features for each data point exceeds the number of training data samples, the SVM will underperform.
• As the support vector classifier works by putting data points, above and below the classifying hyperplane there is no probabilistic explanation for the classification.