PhDEng-Mackenson
Data Scientist/ AI ML Engineer/Quantum Computing Scientist.
UNIVERSITY OF GUADALAJARAMexico, Guadalajara
Pinned Repositories
classiq-library
The Classiq Library is the largest collection of quantum algorithms, applications. It is the best way to explore quantum computing software. We welcome community contributions to our Library 🙌
Deep-Convolutional-Neural-Networks-For-Images-Classification
In this project, we utilized Deep Convolutional Neural Networks to accurately classify images of dogs and cats. Following training, the model effectively distinguishes between the two animals in a given image.
Diabetes-Prediction-Using-Machine-Learning-Algorithms.
In this project, we utilized a variety of Machine Learning Algorithms, including Random Forest, Decision Tree, SVM, and k-Nearest Neighbor, to predict diabetes in patients. We specifically utilized the Pima Indians Diabetes Database for this analysis. Additionally, we employed XBOOST to compare the accuracy of each algorithm.
Logistic-Regression-For-Rainy-Day-Prediction.
In this project, we employed Logistic Regression Algorithms to forecast rain in Australia. Our analysis focused on the Rains in Australia Dataset for accurate predictions.
Model-Selection-Techniques-For-Regression-Problems.
Neural-Network-For-Predicting-Stock-Prices
In this project, we created a neural network to predict the price of a stock. We used TSLA.csv data set.
Neural-Networks-For-Time-series-Forecasting
In this project, we utilized neural networks, specifically LSTM models, to tackle a variety of time series problems, including multivariate forecasting for Multiple Parallel Series.
PhDEng-Mackenson
QML-for-Conspicuity-Detection-in-Production
Womanium Quantum+AI 2024 Projects
Transaction-Analysis-Clustering
This project aims to analyze transaction patterns and detect anomalies using clustering techniques. The dataset includes transaction data from 2013 to 2024, and various clustering algorithms were applied to discover hidden patterns and outliers.
PhDEng-Mackenson's Repositories
PhDEng-Mackenson/2025-IonQ
PhDEng-Mackenson/PhDEng-Mackenson
PhDEng-Mackenson/QML-for-Conspicuity-Detection-in-Production
Womanium Quantum+AI 2024 Projects
PhDEng-Mackenson/classiq-library
The Classiq Library is the largest collection of quantum algorithms, applications. It is the best way to explore quantum computing software. We welcome community contributions to our Library 🙌
PhDEng-Mackenson/Transaction-Analysis-Clustering
This project aims to analyze transaction patterns and detect anomalies using clustering techniques. The dataset includes transaction data from 2013 to 2024, and various clustering algorithms were applied to discover hidden patterns and outliers.
PhDEng-Mackenson/Wine-Quality-Classification.
In this project, I utilized a dataset of wine quality to categorize wines based on their unique compositions. To achieve this classification, clustering algorithms are employed. Additionally, random forest is utilized to determine the importance of each variable in order to classify the wines based on the most significant factors.
PhDEng-Mackenson/Deep-Convolutional-Neural-Networks-For-Images-Classification
In this project, we utilized Deep Convolutional Neural Networks to accurately classify images of dogs and cats. Following training, the model effectively distinguishes between the two animals in a given image.
PhDEng-Mackenson/Neural-Network-For-Predicting-Stock-Prices
In this project, we created a neural network to predict the price of a stock. We used TSLA.csv data set.
PhDEng-Mackenson/Neural-Networks-For-Time-series-Forecasting
In this project, we utilized neural networks, specifically LSTM models, to tackle a variety of time series problems, including multivariate forecasting for Multiple Parallel Series.
PhDEng-Mackenson/Logistic-Regression-For-Rainy-Day-Prediction.
In this project, we employed Logistic Regression Algorithms to forecast rain in Australia. Our analysis focused on the Rains in Australia Dataset for accurate predictions.
PhDEng-Mackenson/Diabetes-Prediction-Using-Machine-Learning-Algorithms.
In this project, we utilized a variety of Machine Learning Algorithms, including Random Forest, Decision Tree, SVM, and k-Nearest Neighbor, to predict diabetes in patients. We specifically utilized the Pima Indians Diabetes Database for this analysis. Additionally, we employed XBOOST to compare the accuracy of each algorithm.
PhDEng-Mackenson/Model-Selection-Techniques-For-Regression-Problems.