mlextend

There are 0 repository under mlextend topic.

• gbalbuena / jupyterlab-workspace

  jupyter-notebookmachine-learningmlextend
  Language:Jupyter Notebook 1

• jeevankande / Association-rules

  Market Basket Analysis is one of the key techniques used by large retailers to uncover associations between items. It works by looking for combinations of items that occur together frequently in transactions. To put it another way, it allows retailers to identify relationships between the items that people buy. Association Rules are widely used to analyze retail basket or transaction data and are intended to identify strong rules discovered in transaction data using measures of interestingness, based on the concept of strong rules.

  apriori-algorithmassociation-rulesmlextend
  Language:Jupyter Notebook 1

• pradeepdev-1995 / Feature-Selection-Techniques

  Feature selection techniques in machine learning is a process of automatically or manually selecting the subset of most appropriate and relevant features to be used in model building. Here we are taking a machine learning regression problem and shows the different steps in feature selection process

  bivariate-analysiscorrelationfeature-engineeringfeature-extractionfeature-selectionfilter-based-feature-selectionlinear-regressionmachine-learningmachine-learning-algorithmsmlextendoutlier-detectionseabornsequential-feature-selectionunivariate-analysisvisualizationwrapper-based-selection
  Language:Jupyter Notebook 1

• resulemreaygan / recommendation-system

  python3pandasmlextendfpgrowthapriori-algorithmaprioriflaskflask-restfulwaitressmatplotlibmatplotlib-pyplotseabornsquarify
  Language:Python 1

• vaitybharati / Assignment-09-Association-Rules-Data-Mining-Groceries-

  Association Rules Data Mining (Groceries). Converting the data frame into a list of lists, Using Transactionencoder to transform this dataset into a logical data frame, Building the data frame: rows are logical and columns are the items that have been purchased, Print Column names, We need to drop nan column from the data frame, Most popular items, Top 10 Popular items, Barplot visualization of popular items, Apriori Algorithm: Association rules with 5% Support and 70% confidence, Association rules with 1% Support and 80% confidence, Visualization of obtained rule.

  pythonpandasnumpyapriori-algorithmassociation-rulesdata-mining-algorithmsseabornmatplotlib-pyplotmlextendfrequent-pattern-miningpreprocessingtransaction-encoderaprioribarplotscatter-plotsupport-confidence
  Language:Jupyter Notebook 1

• abdullahkavakli / Credit-Card-Fraud-Detection-Using-Machine-Learning

  It's a data science project for classification of Fraudulent and Non-Fraudulent transactions.

  aiartificial-intelligencebankingclassificationcredit-cardcreditcarddata-sciencedata-visualizationfinancemachine-learningmachinelearningmlextendpandaspythonseabornsklearntpot
  Language:Jupyter Notebook 0

• tdries / TabPrepPy-BasketCaseSuperstore

  Tableau Prep+Python:Basket Case Analysis with Superstore. Setup: People who bought product X and product Y might be interested in product Z. By analyzing a lot of transactional data we try to distill association rules to make such statements. The output table out the Tableau Prep flow can be implemented in various ways. Tools: Tableau Prep + Python Script (MLextend) Create an association rules (a priori algorithm) table making use of the Superstore dataset.

  tabpypythontableauprepapriori-algorithm-pythonmlextend
  Language:Python 0