A fast and frugal tree classifier for sklearn

The goal of this project was to detect Diabetes using XGBoost based on the information of more than 70,000 patients through the questionnaire that they filled out for the Organization for Disease Control and Prevention.

This Data Science-like project is aimed on analyzing a bunch of techniques for False Negatives (Frauds classified as Non-Frauds) in a extreme imbalanced dataset - Financial Fraud and improving the predictors performance.

TPs de la materia Organización de Datos - Catedra Collinet - FIUBA - 2C2020

Stochastic Gradient Trees implementation in Python

This repository contains materials and course projects during attending the Intelligent Systems Course, for more detailed information please have a look at my Final_Report files which have been separately uploaded for each of the projects and consist of all required information about the implementations, analyses, and anything else you may conce…

Machine Learning Practice Essemble Model Bagging, Using detailed EDA, Preprocessing Scheme, looking model with best performance F1 score, Hyperparamater Tunning for best models, and intrepertation

This repository contains all of the Machine Learning-related projects I've worked on. The projects are part of the undergraduate course at the University of Tehran.

A project to build an ML model on the Cancer Prediction dataset

Aulas do curso Dominando Data Science da Flai.

This Jupyter Notebook serves as a comprehensive guide to performing support vector machine (LinearSVC) classification and calculating accuracy scores for machine learning tasks. It provides step-by-step instructions and code examples for building, training, and evaluating a LinearSVC classifier

This Data Science-like project is aimed on analyzing a bunch of techniques for False Negatives (Frauds classified as Non-Frauds) in a extreme imbalanced dataset - Financial Fraud and improving the predictors performance.

Tensorflow implementation of a decision Tree

AI basic project

Project to classifier persons to die and not die by IA algorithm Decision Tree Classifier

In this paper, we analyze the Forest Cover Dataset provided by UCI, build numerous classifiers to classify the forest cover type of a parcel of land, and evaluate the performance of the classifiers. To achieve this goal, we applied a step by step approach to the problem and explained the process thoroughly. We start the process with the data pre…

Stochastic Gradient Trees implementation in C++, Python

Data science implementation for academic success prediction using C4.5 and Random Forest algorithms.