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 🙌

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/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.