Code for the CUP Elements on text analysis in Python for social scientists
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Updated
Sep 11, 2022 - Jupyter Notebook
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.
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