A dataset with 5077 images of numbered signals and a script to create a train-test-split

A simple example of random state in train test split using python

The purpose of this project was to analyze and predict housing prices using attributes or features such as square footage, number of bedrooms, number of floors, and so on.

A time slicer for training and testing temporally correlated Machine Learning models.

Train a own model using dataset to detect covid-19 positive or negative.

Time series analysis on Yen Futures with ACF, PACF, ADF tests and seasonal decomposition to detect stationary trends. Screen for robust regression models on rolling train-test windows.

Predict Stocks Price

Coursera Speccialization Courses

`Spltr` is a simple PyTorch-based data loader and splitter. It may be used to load arrays and matrices or Pandas DataFrames and CSV files containing numerical data with subsequent split it into train, test (validation) subsets in the form of PyTorch DataLoader objects.

In this project, you work as a Data Scientist for a professional football club. The owner of the team is very interested in seeing how the use of data can help improve the team's performance, and perhaps win them a championship! The draft is coming up soon (that's when you get to pick new players for your team), and the owner wants you to create…

Model-Validation-Methods

This ML⚒ project is to prove the dependencies of a motor🛠 in an everyday pump system👷‍♂️👨‍🏭

Understanding Train-Test-Split in R

Prevendo o Nível de Satisfação dos Clientes do Santander.

GroupSplit is a module to help split datasets into train and test sets for data science and machine learning projects.

In this case study target variables are explored while using two ways of fitting a linear regression model to predict Boston housing prices. The the least squares method is used to estimate the coefficients.

Data preparation, statistical reasoning and machine learning are used to solve an unbalanced classification problem. Different techniques are employed to train and evaluate models with unbalanced classes.

This is the classification of the titanic data using logistic regression

Machine Learning exercise to create models capable of classifying candidate exoplanets from a dataset.