Website sources for Applied Machine Learning for Tabular Data

Code for the CUP Elements on text analysis in Python for social scientists

Lead Scoring is such a powerful metric when it comes to quantifying the lead & it is nowadays used by every CRM. In this repository, we are going to take a look at the UpGrad lead scoring case study and see how can we solve this problem through several supervised machine learning models.

MachineShop: R package of models and tools for machine learning

This repository contains the code and datasets for creating the machine learning models in the research paper titled "Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach"

Framework to evaluate Trajectory Classification Algorithms

A scikit-learn compatible hyperbox-based machine learning library in Python

En este proyecto de GitHhub podrás encontrar parte del material que utilizo para impartir las clases de Introducción a la Ciencia de Datos (Data Science) con Python.

This project aims to analyze and classify a real network traffic dataset to detect malicious/benign traffic records. It compares and tunes the performance of several Machine Learning algorithms to maintain the highest accuracy and lowest False Positive/Negative rates.

IntelELM: A Python Framework for Intelligent Metaheuristic-based Extreme Learning Machine

A tool to support using classification models in low-power and microcontroller-based embedded systems.

Sentiment analysis of Tokopedia app users on Google PlayStore using the Support Vector Machine (SVM) method

Projet-PI-4DS2

MetaPerceptron: A Standardized Framework For Metaheuristic-Driven Multi-layer Perceptron Optimization

A repository dedicated to storing guided projects completed while learning data science concepts with Dataquest.

Repository for several data science and analysis projects

A graphical machine learning program written with tkinter and scikit-learn library.

Machine learning system for predicting genetic disorders using genomic, clinical, and demographic data. Implements robust preprocessing, feature selection, and multi-model classification (RF, XGBoost, LightGBM, CatBoost) with cross-validation to support early, data-driven genetic risk assessment.

In this project I used ML modeling and data analysis to predict ad clicks and significantly improve ad campaign performance, resulting in a 43.3% increase in profits. The selected model was Logistic Regression. The insights provided recommendations for personalized content, age-targeted ads, and income-level targeting, enhancing marketing strategy.