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.

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

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.

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.

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.

everything about the code is explained within the code file .ipynb itself using comments above or below the code.

Developing, Evaluating, and Comparing different Classification Models on Heart Failure Prediction Dataset

Advanced Machine Learning