In this Notebook , We are going to solve the Loan Approval Prediction.This is a Classification problem in which we need to classify whether the loan will be approved or not.
A loan is a bank's main source of revenue. The profits earned through loans account for most of the bank's profits. Even though the bank accepts the loan following a lengthy verification and testimony process, there is no guarantee that the chosen candidate is the right one. When done manually, this operation takes a long time. We can predict whether a given hopeful is safe or not, and the entire testimonial process is automated using machine literacy. Loan Prognostic is beneficial to both bank retainers and hopefuls.
The Bank wants to automate the loan eligibility process (real-time) based on customer detail provided while filling out online application forms. These details are Gender, Marital Status, Education, number of Dependents, Income, Loan Amount, Credit History, and others.
To automate this process, they have provided a dataset to identify the customer segments that are eligible for loan amounts so that they can specifically target these customers.
As mentioned above this is a Binary Classification problem in which we need to predict our Target label which is “Loan Status”.
Loan status can have two values: Yes or No.
Yes: if the loan is approved No: if the loan is not approved So using the training dataset we will train our model and try to predict our target column that is “Loan Status” on the test dataset.
Among all industries, insurance domain has the largest use of analytics & data science methods. This data set would provide you enough taste of working on data sets from insurance companies, what challenges are faced, what strategies are used, which variables influence the outcome etc. This is a classification problem. The data has 614 rows and 13 columns.
-
Click this DataSet Link 👉 Loan_Status-Prediction
-
I have also uploaed the csv file in my repository 👉 Loan_Status-Prediction
Build the six classification model is Logistic Regression, Suport Vector Machine, Decision Tree, Random Forest, K-Nearest Neighbour and Naive Bayes.
Highest Accuracy is 81% of the Logistic Regression.
Youtube channel Siddhardhan