Loan Default Risk Prediction Machine Learning Project

In this project, we are going to predict how capable each applicant is repaying a loan.

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What will you learn

  • Understanding the problem statement and importing the file

  • Initializing the libraries and understand it's use

  • Using info and describe the function and extracting information from the results

  • Checking for the null values and performing necessary imputations

  • Plotting histogram and bar plot for numerical versus target variable for advanced EDA

  • How to analyze categorical variables using graphs

  • How to plot heatmap and FacetGrid in seaborn

  • Creating new features from existing features (Feature Engineering)

  • Understanding One Hot and Label encoding and it's implementation

  • Applying ensembling method Random Forest and extracting important features using feature_importance function

  • Difference between Deep learning model and the ML model

  • Creating a function for extensive Feature Engineering and Pre-processing of the Dataset

  • Preparing dataset for LightGBM

  • Initializing parameters for LightGBM

  • Selecting the right metrics according to the Dataset

  • Training the model and making predictions

  • Plotting graphs different metrics and models to select the best one out

Project Description

Home Credit makes use of a variety of alternative data--including telco and transactional information--to predict their clients' repayment abilities. Many people struggle to get loans due to insufficient or non-existent credit histories. And, unfortunately, this population is often taken advantage of by untrustworthy lenders. Home Credit strives to broaden financial inclusion for the unbanked population by providing a positive and safe borrowing experience.

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