Isabela's Projects
Projeto de uma Agenda com Django. Este projeto faz parte de um curso de Python, nível intermediário.
The purpose of this repository is to process data , prepare it, and build models to predict certain activities using ML techniques. The entire process leverages PySpark for distributed data processing . The codes were developed according to Advanced Machine Learning and Signal Processing and Applied Ai with deeplearning courses from IBM.
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
Containers Template
Credit Card Fraud detection with neural networks(anomaly detection) and machine learning techniques (random forest classifier)
Testing Advanced SQL queries and integrating classical databases in quantum processors (hybrid algorithm)
Data Engineering - ONS and Eneva. Eneva (ENEV3:BZ), listed in the new market segment of the B3 (Brazilian Stock Exchange), is one of Brazil's leading integrated energy companies.
Data Visualization
Testing SAS and Python codes for decision tree
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.
Visualizing, Munging and Predicting Stock Prices using machine learning models in R and Python. Course Python statistics - financial analysis. I developed and evaluated two different models, LSTM and Random Forest to predict the stock prices of Apple Inc. based on historical data.
Personal notes - Financial Market Course from Yale University
Financial Risk Management with R, Duke University
Course: Introduction to scientific computation - Final project HPC- Creation of container and parallel job using OpenMP and MPI
Using IDA historical data to predict the country which has a probability greater than 0.1 of being in debt with IDA.
Introdução Django
Lecture presented in the course Inglês em Contexto Acadêmico
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Assignment 3 - Course Applied Machine Learning
Multidimensional Expressions. MDX is used specifically for querying multidimensional data structures. MDX queries result in multidimensional data structures (cubes or cellsets).
Qutip Monte Carlo to solve the dynamics of time-local non-Markovian master equations