In this notebook, I have done Data Cleaning, Data Wrangling, EDA and Feature Engineering. After that I trained the dataset using Machine Learning Algorithm Random Forest Regressor.

BitPredictor - A cutting-edge machine learning-based solution for predicting cryptocurrency prices. Harnessing the power of advanced algorithms and data analysis techniques, this system aims to provide accurate and timely forecasts for Bitcoin and other cryptocurrencies.

This repository contains the LifeExpectancy Prediction Project, a comprehensive data science project aimed at predicting life expectancy based on various health, economic, and social factors. The project includes steps for data preprocessing, exploratory data analysis (EDA), model selection, training, hyperparameter tuning, and model interpretation

Files relevant for my bachelor thesis on different automatic emotion recognition approaches

Using Random Forest to detect Malicious attacks on the ES6 Website

An application that allow the user to log in (and access to all his data), and connect to external distributors, in order to get the coffee generated by a Machine Learning algorithm

XGBoost Predictive Model for TikTok's Claim Classification: EDA, Hypothesis Testing, Logistic Regression, Tree-Based Models

Rate your music out of 5 stars (interactive)!

Estimación de turbidez en el agua a la entrada de la planta de tratamiento de SAMEEP, utilizando los productos Sentinel-2 MSI L2A y aprendizaje automático.

⛳️ This project, within the course Sports Analytics, TDDE64, at Linköping University, uses Random Forest and SVM models to predict tournament outcomes, revealing insights into the factors that drive player success in golf.

🍊 📊 💡 Orange: Interactive data analysis

Forecasted Airbnb 'Super host' status in Chicago with an 84% accuracy using Logistic Regression and assessed potential returns on investment employing the Herfindahl Index for strategic investment insights

Optimized Craigslist's classification system by creating an algorithm combining LSTM and Random Forest for Text and Image Classification respectively

Predicted consumer activity type for the NCAA Women’s Basketball Tournament

Machine Learning para analisis de Encuestas de Hogares. Modelos de Random Forest para predecir caracteristicas de hogares argentinos usando EPH y datos del Censo.

This study uses predictive analytics to detect stroke risk factors early, aiming to reduce occurrences. By analyzing risk factors with machine learning, it uncovers patterns and correlations. Models such as Logistic Regression, KNN, Decision Trees, Random Forest, and Neural Network.

A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.

A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.