Using machine learning methods to identify fake banknotes using measurements
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Updated
Sep 28, 2022 - Jupyter Notebook
Using machine learning methods to identify fake banknotes using measurements
Logit is a comprehensive Git management tool that provides an interactive command-line interface for executing a wide range of Git operations.
This is the source code (R) for my Bachelor's thesis for the degree of Economics, BA in Universidad San Francisco de Quito (Ecuador). Between 2014-2016, corruption tolerance greatly increased in Ecuador. Why? In memory of the late Jorge Pazmiño (1941-2021).
**Intro to Statistics**: Discover the fascinating world of statistics with this engaging and accessible guide. Perfect for beginners, this book covers everything from p-values and t-tests to machine-learning models like random forests. Learn through practical examples and R code snippets, and unlock the power of statistical analysis.
Project for the ING Lion's Den Competition. The goal of this project is to predict defaults of Big Lion Bank’s customers and I build logit, ridge, KNN and XGBoost models.
Comparing Logistic Regression and ML algorithms for predicting defaults on consumer loans.
Python Code for MKT - Consumer and Brand Research Course for Simon Business School
Logit-space logical activation functions for pytorch
To analyze the factors influencing student acceptance at the University of Rochester
Standard logistic function.
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ML models for HR classification problem. For more information please visit the link: https://datahack.analyticsvidhya.com/contest/wns-analytics-hackathon-2018-1/
This Rmarkdown document is a brief description on Logistical Regression. Typos and changes welcomed via git pull requests
Collection of Jupyter Notebooks of small tasks regarding the lecture 'Multivariate Statistical Procedures' at KIT.
Binary Classification Usinig 6 Machine Learning Tools, R, and Rmarkdown by Matt Curcio
Generación de reportes para los mejores modelos Logit obtenidos con el paquete glmulti
Classification of diabetes patient using Linear & Quadratic Discriminant analysis, Logit, Random Forest, Support Vector Machine and KNN in R.
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