Loan Status prediction +(EDA and Modeling)

+ Exploratory Data Analysis and Modeling

Problem Statement

Dream Housing Finance company deals in all kinds of home loans. They have presence across all urban, semi urban and rural areas. Customer first applies for home loan and 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 provided a dataset to identify the customers segments that are eligible for loan amount so that they can specifically target these customers.

screenshot from google

Dataset Link: Click here.

Data Dictionary.

Let’s jump into the code.

install necessary tools.

!pip install pywaffle — q

Import necessary libraries.

Load Dataset.

Imputing Missing value

Categorical column fill with mode

numerical column fill with median

fillnan value from Numerical columns

EDA (Exploratory Data Analysis)

Univariate Analysis

Categorical Column

“Target Column”

“Gender Column”

“Married Column”

“Education Column”

Numerical Column

“ApplicantIncome”

For Other Columns and Bivariate Analysis check the notebook Click here.

Spliting data into train and test

Model Building

Check the notebook for Feature Engineering Check Here.

Model Building II

Logistic Regression ROC Curve.

Decision Tree Classification ROC Curve.

XGboost Hyperparameter Tuning.

Check the notebook for Auto ML Code. Open Notebook.