Lending Club Loan data analysis

Modeled the credit risk associated with consumer loans. Performed exploratory data analysis (EDA), preprocessing of continuous and discrete variables using various techniques depending on the feature. Checked for missing values and cleaned the data. Built the probability of default model using Logistic Regression. Visualized all the results. Com…

Prediction of loan defaulter based on more than 5L records using Python, Numpy, Pandas and XGBoost

L&T Financial Services & Analytics Vidhya presents ‘DataScience FinHack’ organised by Analytics Vidhya

Applying machine learning to predict loan charge-offs on LendingClub.com

A Classification Problem which predicts if a loan will get approved or not.

Loan Management System and daily collection Api For Financial institutions.

Loan Default Prediction using PySpark, with jobs scheduled by Apache Airflow and Integration with Spark using Apache Livy

The project provides a complete end-to-end workflow for building a binary classifier in Python to recognize the risk of housing loan default. It includes methods like automated feature engineering for connecting relational databases, comparison of different classifiers on imbalanced data, and hyperparameter tuning using Bayesian optimization.

Predicting the default customers

[Project repo] Improving business with a credit risk model

Capstone Project: Predicting default in P2P lending

Classification problem to predict loan defaulters using Lending Club Dataset

L&T Financial Services & Analytics Vidhya presents ‘DataScience FinHack’ organised by Analytics Vidhya

📘 Detailed Exploratory Data Analysis of Lending Club Loan Data

The objective of this project is to predict the probability of borrower defaulting on a vehicle loan in the first EMI (Equated Monthly Installments) on the due date.