Predicting Loan Default

In this project, we will automate the loan eligibility process (real-time) based on customer details while filling the online application form.


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What will you learn

  • Understanding the Problem Statement and Importing the Dataset

  • Performing basic EDA to get Insights into the data

  • Importing the necessary libraries

  • Using Info function to check for null values and datatypes

  • Imputing null values using suitable methods

  • Converting categorical values into numerical vectors

  • Plotting barplot of the dependent variable versus Independent variable

  • Using Boxplot for identifying outliers

  • Seperating dependent and Independent columns for training the model

  • Using train_test_split function for creating training and testing dataset

  • Understanding and Implementing Standardization

  • Applying ensemble model using Random Forest Classifier

  • Applying Decision Tree Classifier using AdaBoost

  • Applying ensembling model Voting Classifier

  • Applying Liner Model Logistic Regression

  • Plotting graphs for weight coefficients for different variables

  • Defining a function for performing Cross-Validation and calculating accuracy simultaneously

  • Applying Gradient Boosting Classifier and feature selection to extract best features for GBC

  • Extracting best features for Random Forest Classifier

  • Using the selected features for training the final model

  • Making predictions using the trained model and saving the predictions

Project Description

About Company
Dream Housing Finance company deals in all home loans. They have a presence across all urban, semi-urban and rural areas. Customer first applies for the home loan after that company validates the customer eligibility for the loan.

The 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 customer's segments, those are eligible for loan amount so that they can specifically target these customers. Here they have provided a partial data set.


  1. Anaconda Continuum Python 64-bit
  2. Seaborn for visualization


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Curriculum For This Mini Project

02h 19m