aayushmnit/cookbook

A repository of machine learning codes written for re-usability

Cookbook

A repository of data science codes written for re-usability and reducing the time taken from Idea to execution in developing a data science solution. This repository contains codes and sample notebooks to how to use these codes in building a data science solution.

Codes -

Generic Preprocessing - Helper functions to do EDA, missing value analysis & treatment and generic preprocessing like (scaling, encoding etc.)

Machine learning - Classification - Helper functions to solve classification type problems in machine learning. It contains codes for holdout/cross validation, model explaination codes (LIME and variable importance plot), and codes for general classification algorithms (Xgboost, LightGBM, Extra trees, random forest, logistic regression, decision trees, K-nearest neighbours and SVM)

Machine learning - Regression - Helper functions to solve regression type problems in machine learning. It contains codes for holdout/cross validation, model explaination codes (LIME and variable importance plot), and codes for general regression algorithms (Xgboost, LightGBM, Extra trees, random forest, linear regression, regression trees, K-nearest neighbours and SVM)

Recommender systems - Helper functions to build recommender systems using Matrix factorization using LightFM package.

Natural language processing - Helper functions for NLP text processing and analysis like N-grams, wordcloud, tokenization, lowercasing, punctuation/stopwords removal, stemmer/lemmatizer, TF-IDF & count vectorizer

Notebooks -

Classification examples - Example notebooks to solve a classification problem

Regression examples - Example notebooks to solve a regression problem

Recommender systems - Example notebooks to build recommender systems