A series of notebooks on Support Vector Machine algorithm

This is the notebook for the titanic dataset in Kaggle. The task is to predict whether a given person id find out whether person is alive or dead.

Repository with natural language processing implementations in Jupyter Notebooks

A Jupyter Notebook with the analysis and prediction of Final Grades (Pass/Fail) for students of mechatronics engineering in several mechanic courses.

The notebook shows how deep learning tools (TensorFlow/Keras and PyTorch ) work in practice.

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.

In this notebook, we perform a binary classification on chest X-ray images to determine whether a person has healthy lungs or is diagnosed with pneumonia. For this classification, we used a custom deep convolutional neural network (CNN) model and achieved an accuracy of 95% on the test set.

A Jyupter notebook for identifying whether the sound and image belong to the same digit using Convolution Neural Networks (multiple models)

This notebook looks into using various python-based machine learning and data science libraries in an attempt to build a ML model capable of predicting whether or not someone has heart disease based on their medical attributes.

This repo consists of Jupyter Notebooks with different versions in different commits.... as part of my work regarding a contest I participated in August... on Kaggle. The competition is about predicting the edibility of Mushrooms depending on various characteristics.

This repository serves as a personal practice space for mastering PyTorch fundamentals. It contains notebooks and code snippets covering essential PyTorch concepts, including tensors, autograd, neural networks, and more. The repository aims to help learners develop hands-on experience with PyTorch through practical exercises and projects.

Build machine learning model to predict whether a house will sell or not based on a set of features. The results will be presented in the form of interactive widgets in jupyter notebook for technical audience that can be used to make informed decision about selling their properties.