Tutorial on how to perform feature encoding, feature scaling, and missing values imputation using the scikit-learn library

Exemplary, annotated machine learning pipeline for any tabular data problem.

first-person activity recognition

Multiple methods to (quickly) encode factor variables, using data.table

Obesity severity level prediction based on different features.. these imp features are extracted then trained using polynomial regression

Predicting Hotel Booking Cancellation with Machine Learning

This project uses predictive analytics to optimize marketing strategies by forecasting customer subscriptions to term deposits. It involves collecting and preprocessing data, training a model, and assessing its performance. Ongoing evaluation ensures adaptability to changing market dynamics, providing valuable insights for marketing analysis

SAS Macro examples for the Blog Post "5 Categorical Encoding Techniques in SAS"

churn prediction using machine learning classification models

Classification of movies as 'Fresh', 'Rotten', 'Certified-Fresh' using categorical predictors as well as review sentiment. Performed feature encoding and used Decision Tree, Random Forest Classifiers. Tackled class imbalance issues by assigning weights to classes. Used tokenization to generate word vectors for reviews to predict movie status.

A machine learning project that predicts car prices based on a dataset.

👩🏻‍🍳🍽️Restaurant Success Prediction using ML

Unofficial Pytorch Implementation of 'Uncorrelated feature encoding for faster style transfer'

Categorical Feature Encoding using Logistic Regression

This project shows a guide for improving the accuracy of regression model.