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

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.

Website sources for Applied Machine Learning for Tabular Data

MachineShop: R package of models and tools for machine learning

Repository for several data science and analysis projects

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 is the repository of Hamoye 2022 Summer Internship Capstone Project by Neural-Network Team

First rank winner in the Machine Learning Course Competition for class 2021-2022. Airline ticket price prediction from end to end (analysis - preprocessing - modeling - testing - deployment - documentation) between Indian cities

Neuronal morphology preparation and classification using Machine Learning.

This project aims to identify, build and tune robust classification models to effectively classify quality red wines using on their characteristics.

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

The repository contains exercises on Machine Learning algorithms in R, using RStudio. Used to dive into ML, data preprocessing, data visualisation, and data exploration.

Basics of classification and bias

Using Classification Models with cross-validation and hyperparameter tuning to predict shoppers decision to make online purchase.

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

Save time by copying and pasting template for ML Models

Comparison between model when using for IMDB classification

This project is a network intrusion detector that uses machine learning algorithms to distinguish between bad (intrusions/attacks) and good (normal) connections. The UI is built using Flask framework and the KDD Cup 1999 dataset is used for training. Python, Flask, and Scikit-learn are the main technologies used in this project.

📊 📋 Bioconductor R package with Graphical Interface to handle multi-omic data by Classification, Enrichment and Network inference.