Loan prediction model using python

By Shalini Sinha

A loan prediction model using python to automate the decision-making process with analysing and fitting various cases provided in the dataset.

Using a dataset with various features related to loan applicants, the goal is to develop a loan prediction model. The objective is to develop a model that can accurately forecast whether or not a loan application will be approved. 

We first imported necessary components and libraries, such as numpy and pandas for data manipulation and seaborn and matplotlib for visualizing trends, further preprocessing, and cleaned the dataset. Whenever necessary, we handled missing values, encoded categorical variables, and standardized/normalized numerical features.

Further to train the model and take various parameters into consideration we used a decision tree Classifier for both classification and regression tasks.  

We hence draw the conclusion that having a precision of more than 80% is a good model. 

which draws one more conclusion that preprocessing the data and removing irrelevancy can make our prediction more accurate based on any dataset provided hence preprocessing is an essential step.