Lending Club Loan data analysis
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Updated
Jul 8, 2019 - Jupyter Notebook
Lending Club Loan data analysis
Prediction of loan defaulter based on more than 5L records using Python, Numpy, Pandas and XGBoost
A Classification Problem which predicts if a loan will get approved or not.
L&T Financial Services & Analytics Vidhya presents ‘DataScience FinHack’ organised by Analytics Vidhya
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…
Applying machine learning to predict loan charge-offs on LendingClub.com
Loan Management System and daily collection Api For Financial institutions.
Predicting the default customers
Capstone Project: Predicting default in P2P lending
Classification problem to predict loan defaulters using Lending Club Dataset
Machine Learning Key Projects
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.
Streamlit_ML_DL
[Project repo] Improving business with a credit risk model
Create a model that predicts whether or not a loan will be default using the historical data.
This repository contains a starter notebook for the DSN AI Bootcamp Qualification Hackathon
Loan Default Prediction, Individual Level Loan Data, Machine Learning, Logistic regression, Ridge, LASSO, Gradient Boosting, SVM, Random Forest
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