Dream Housing Finance company deals in all home loans. They have presence across all urban, semi urban and rural areas. Customer first apply for home loan after that company validates the customer eligibility for loan. Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have given a problem to identify the customers segments, those are eligible for loan amount so that they can specifically target these customers.

This is a standard supervised classification task.A classification problem where we have to predict whether a loan would be approved or not. In a classification problem, we have to predict discrete values based on a given set of independent variable(s).Classification can be of two types:

Supervised: The labels are included in the training data and the goal is to train a model to learn to predict the labels from the features.

Binary Classification : In this classification we have to predict either of the two given classes. For example: classifying the gender as male or female, predicting the result as win or loss, etc.

Multiclass Classification : Here we have to classify the data into three or more classes. For example: classifying a movie's genre as comedy, action or romantic, classify fruits as oranges, apples, or pears, etc.

Loan prediction is a very common real-life problem that each retail bank faces atleast once in its lifetime. If done correctly, it can save a lot of man hours at the end of a retail bank.

Although this notebook is specifically built to give you a walkthrough of Loan Prediction problem, you can always refer the content to get a comprehensive overview to solve a classification problem.