Slides for the "Interpretable SDM with Julia" workshop

This is a repository with the assignments of IE675b Machine Learning course at University of Mannheim.

Life Expectancy Prediction using Random Forest Regressor with Cross Validation and Hyperparameter Tuning

Practice sessions for the course "Machine Learning and Data Mining" in the Faculty of Mathematics and Informatics, Sofia University.

Contain different Machine Learning algorithms.

Atividades práticas elaborados em Python, no ambiente Google Colab, para a disciplina de ferramentas de inteligencia artificial

Stroke: Statistical analysis of risk factors and creation of predictive models using machine learning

Time based splits for cross validation

A high-level machine learning and deep learning library for the PHP language.

Automated fingerprint classification using the Socofing dataset, leveraging machine learning to categorize fingerprints into arches, loops, and whorls for enhanced biometric identification. Project covers data analysis, model development, and evaluation to improve security and authentication systems.

R package for machine learning classification model evaluation.

Implementation of SvF-technology of balanced identification of mathematical models by experimental data

Data Enthusiast | Predictive Modeler | Turning Insights into Strategies

This project leverages advanced machine learning algorithms to detect and classify malicious emails, focusing on spam and phishing threats. As email threats grow more sophisticated, accurate detection is critical to ensuring the security and privacy of both individuals and organizations.

Data Enthusiast | Predictive Modeler | Turning Insights into Strategies

Spatial error estimation and variable importance

This is a machine learning project that uses various machine learning algorithms to predict whether a patient is diabetic or not. Here various machine learning algorithms like SVM, RF Classifier, DT Classifier, KNN, LR , LRwith CV, NB Classifier, and XGB are used. For this work, a website is made with Python Streamlit library. Paper is ongoing.

Using 1994 Census data to predict whether a person's salary meets the threshold for upper middle class. Fit a logistic regression model (min and optimal lambda), and decision tree for classification.

Through the AlexNet and VGG16 convolutional networks, the neural networks were trained on a set of 600 spatial spectrogram images divided into 3 categories and divided into train, test, validation test

Spaceship Titanic Classification