Supervised (ML) Regression - NYC Taxi Trip Time Prediction, based on 2016 NYC Yellow Cab published by Taxi & Limousine Commission (TLC).
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Updated
Mar 24, 2023 - Jupyter Notebook
Supervised (ML) Regression - NYC Taxi Trip Time Prediction, based on 2016 NYC Yellow Cab published by Taxi & Limousine Commission (TLC).
EDA, Regression Analysis , Classification & Non-Parametric inference Problems
An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code
Project to predict retention of students in a study program up-to and beyond semester 6 based on scores, socio-economic & demography factors (like debt, gender, religion and race), transferred credits, family fee contributions, academic background, phone and email habits.
This project focuses on modelling industrial copper data using Python and various libraries such as pandas, numpy, scikit-learn
The idea behind this Intro to Machine Learning Guide was to initially create a list of resources to provide to my students. This eventually morphed into a comprehensive guide that will eventually cover everything from Linear Regression to Neural Networks
Análise de um estudo de coorte sobre pessoas com câncer de tiróideo. Realização de análise descritiva, aplicação de testes estatísticos e desenvolvimento de um modelo de regressão logístico com desfecho remissão do câncer. Atividade ministrada pela professora Nataly Jimenez
Hamoye Data Science Cohort 2021
A simple R program that implements a very basic Polynomial Regression on a small data set. Because these data set don't have liner relationship between independent variable and dependent variable. so if we use the liner model then well get very High error. so in these example w'll compare both the model and select which one is best.
What's the Weather Like?
machine learning a to z udemy course
"Excited to share my latest project on LinkedIn: a crop yield prediction ML model deployed with Streamlit! 🌱 Leveraging the power of Stochastic Gradient Descent regression(SGD) algorithm, this tech-driven solution boasts an impressive 94% accuracy on both training and testing data.
Forecasting stock market and simulating buy/sell stock market using 5 classifications and 5 regression models, as well as 5 data sets.
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