Exemplary, annotated machine learning pipeline for any tabular data problem.
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Updated
Aug 30, 2019 - Jupyter Notebook
Exemplary, annotated machine learning pipeline for any tabular data problem.
Tutorial on how to perform feature encoding, feature scaling, and missing values imputation using the scikit-learn library
Categorical Feature Encoding using Logistic Regression
Multiple methods to (quickly) encode factor variables, using data.table
first-person activity recognition
Obesity severity level prediction based on different features.. these imp features are extracted then trained using polynomial regression
churn prediction using machine learning classification models
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
Predicting Hotel Booking Cancellation with Machine Learning
SAS Macro examples for the Blog Post "5 Categorical Encoding Techniques in SAS"
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.
Unofficial Pytorch Implementation of 'Uncorrelated feature encoding for faster style transfer'
This project shows a guide for improving the accuracy of regression model.
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