Isabela Dias's Projects
Projeto de uma Agenda com Django. Este projeto faz parte de um curso de Python, nível intermediário.
The model predicts the treatment success rate for new TB cases with high accuracy and robustness. Two different approaches: PCA and Bayesian Inference. The Bayesian regression analysis reveals that c_new_sp_tsr and new_sp_fail are significant predictors of the treatment success rate, while other predictors show less certainty in their effects.
Simulation of the BB84 Protocol
Sistema Web para cadastros de currículos online. Criado utilizando PHP em arquitetura MVC.
Cleaning data using decision tree and k-nn techniques
Credit Card Fraud detection with neural networks(anomaly detection) and machine learning techniques (random forest classifier)
Deep Learning concepts and techniques: Regularization, Epochs, Batch,Hyperparameters, Cross validation, Optimizers
Cleaning data using data analysis and exploratory analysis techniques
In this project, I explore the expedient and stringent protocols, both quantum error correction protocols designed to protect quantum data from errors.
Course: Introduction to scientific computation - Final project HPC- Creation of container and parallel job using OpenMP and MPI
The International Development Association (IDA) credits are public and publicly guaranteed debt extended by the World Bank Group. IDA provides development credits, grants and guarantees to its recipient member countries to help meet their development needs.
Introdução Django
Lecture presented in the course Inglês em Contexto Acadêmico
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Assignment 3 - Course Applied Machine Learning
Qutip Monte Carlo to solve the dynamics of time-local non-Markovian master equations
Project presented to the course Applied Plotting, Charting & Data Representation in Python, University of Michigan
Assignment 3 - Course Database Management Essentials, University of Colorado System
Official repository for Citation Style Language (CSL) citation styles.
OLS. R and Python. In this project, we study fundamental concepts of Supervised ML models, such as Regression Analysis: Coefficient of Model Adjustment (R²), Parameters Estimation ,Statistical Significance of the Model (F test, T test) ,Multiple Regression , Qualitative Explanatory Variables (X) , heteroscedasticity and etc.
Scripts in R.Logistic Models. In this project, we explore theoretical foundations, Model specification and canonical connection functions, Binary and multinomial logistic models, Estimation of parameters by maximum likelihood, Cutoff, sensitivity, specificity, ROC curve and GINI index
Improving system security using neural networks
HTML - CSS - Website Template . Atividade entregue - Agenda 14- do Curso Desenvolvimento de Sistemas
In this code, I reproduce the results from Ref. Entropy 2021, 23(2), 179;
Unsupervised Machine Learning techniques (R and Python): CLUSTERING, FACTOR ANALYSIS AND CORRESPONDENCE ANALYSIS
Python scripts using the Visualization Toolkit (VTK) and Topology ToolKit (TTK) libraries. Tasks: visualize and explore topological features of a 3D volume and 2D scalar field datasets. 1. Probability density for the 3d electron position in a hydrogen atom and 2. 2D scalar field.