Explanation and Game – Play Capabilities of Modern AI and Large Language Models Project
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Updated
Aug 20, 2023 - Jupyter Notebook
Explanation and Game – Play Capabilities of Modern AI and Large Language Models Project
I used this notebook to discuss different supervised learning approaches. In the notebook you can find evaluations of a logistic regression, a K-Nearest-Neighboor, a Support Vector Machine, a Decision Tree and the ensemble methods Random Forest, AdaBoost and XGBoost Classifyer.
This project develops a predictive model and a decision support system for evaluating the risk associated with Home Equity Line of Credit (HELOC) applications. It features an interactive interface for financial institutions, integrating multiple models for transparent and effective decision-making.
Regression problem predicting Boston house prices in RStudio
We investigated the performance of the K Nearest neighbours and the Decision Tree machine learning models. We compared them based on their classification accuracy on the UCI Hepatitis and Diabetic Retinopathy datasets.
This project analyzes Brent oil prices from 1987-2022, detecting structural changes and associating them with major events to provide data-driven insights for the energy industry.
End-to-End Machine Learning project I made as a machine learning intern @ Mentorness
Learn about Feature Engineering and get familiar with Advanced regression techniques like Lasso, ElasticNet, Gradient Boosting, etc.
We investigated the performance of the Logistic and Multiclass Regression models and compared their accuracies to KNN. We compared Logistic Regression and KNN based on the "IMdB reviews" dataset, while Multiclass Regression and KNN were compared based on the "20 news groups" dataset.
This project involves a classification task using various machine learning algorithms on a heart disease dataset. The dataset comprises features related to heart health, with the target variable indicating the presence or absence of heart disease.
G-PhoCS is a software package for inferring ancestral population sizes, population divergence times, and migration rates from individual genome sequences.
View-based development and model consistency framework
This repository contains my diabetes classification model comparison project using Python
This project demonstrates the use of both Linear and Polynomial Regression models to predict salaries based on the position level of employees. By using a dataset that contains position levels and their corresponding salaries, this project showcases the differences between linear and polynomial models in fitting data and making predictions.
Decision Tree and Random Forest model were used to train and test the data . Classification report and confusion matrix were used to evaluate the model
Automatic, efficient and flexible implementation of complex machine learning pipeline generation and cross-validation.
DRAB (differential regulation analysis by bootstrapping) is a method for identifying genes with context-specific patterns of local genetic regulation.
This project pioneers advancements in Natural Language Processing (NLP) by introducing a deep neural network approach to address challenges in interpreting multi-word sentences, detecting idioms, and classifying text. Focusing on text classification, it evaluates various models, with BERT outperforming others with an F1 score of 0.7980.
Repository for my Master's thesis comparing volatility models.
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