Build and evaluate several machine learning algorithms to predict credit risk
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Updated
Mar 6, 2022 - Jupyter Notebook
Build and evaluate several machine learning algorithms to predict credit risk
I'll use various techniques to train and evaluate a model based on loan risk. I will use a dataset of historical lending activity from a peer-to-peer lending services company to build a model that can identify the creditworthiness of borrowers.
For this project, I predicted credit risk with the supervised machine learning models I built and evaluated using Python.
This repo represents all the resampling techniques needed to achieve better results in highly unbalanced or skewed data that has 77 % of data in one class and rest in others.
Use the different techniques of resampling to quantify the uncertainty of predictions for a KNN regressor
In this analysis we build and evaluate several machine learning algorithms by resampling models to predict credit risk.
Machine learning used to predict the loan risks and classify them as healthy loans or high-risk loans.
Classify stars, galaxies, and quasars with SDSS DR16 data. Balanced dataset using resampling techniques improves AdaBoost classifier's performance, enhancing astronomical object classification accuracy.
Binary trait Resampling method Adjusting for Sample Structure
CLI Image Processing and Editing Suite
Repository contains the assignment that I have implement under the subject Image processing and pattern analysis.
Exercises in Machine Learning with R
Python for Geosciences: Basic python, Numpy, Matplotlib, Pandas, Data analysis, Data Visualization
Predict credit risk with machine learning models by using different techniques to train and evaluate models with unbalanced classes.
Instructional materials (course files) for the BBT4206 course (Business Intelligence II) using R. Topic: Resampling Methods.
Machine Learning algorithms and code snippets in Python
PhD Thesis (TeX) on Data Mining Temporal and Indefinite Relations using Numerical Dependencies. University College London
Unit 11 Homework - Risky Business
This is an ongoing project on Characterizing, calculating and understanding(the drivers of) Elevational Migration of resient birds in the southern western Ghats.
Pattern Jitter is an algorithm for generating artificial spike trains that are maximally random while preserving the smoothed firing rates and the recent spike histories in a recorded spike train.
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