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

Website sources for Applied Machine Learning for Tabular Data

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"

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.

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.

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

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.

Projet-PI-4DS2

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

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

Repository for several data science and analysis projects

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

Neuronal morphology preparation and classification using Machine Learning.

MetaPerceptron: Unleashing the Power of Metaheuristic-optimized Multi-Layer Perceptron - A Python Library

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.

Successfully established a machine learning model which can predict whether any given water sample is potable or not, based on its set of various properties, to a considerably high level of accuracy.