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andi611 / Apriori-and-Eclat-Frequent-Itemset-Mining

Star 49

Implementation of the Apriori and Eclat algorithms, two of the best-known basic algorithms for mining frequent item sets in a set of transactions, implementation in Python.

python data-mining gpu gcc transaction cuda plot transactions gpu-acceleration apriori frequent-itemset-mining data-mining-algorithms frequent-pattern-mining apriori-algorithm frequent-itemsets pycuda gpu-programming eclat eclat-algorithm
  • Updated Dec 12, 2018
  • Python

jaydipkumar / Data-Science-Python

Star 32

Data Science Python Beginner Level Project

neural-network random-forest machine-learning-algorithms datascience pca artificial-neural-networks convolutional-neural-networks kmeans support-vector-machines decision-tree nlp-machine-learning clustering-algorithm forecasting-models machine-learning-tutorials xgboost-algorithm machine-learning-projects knn-algorithm eclat-algorithm apriori-algorithm-python machine-learning-projects-beginners
  • Updated Jul 11, 2020
  • Jupyter Notebook

fatihsen20 / Frequent-Mining-Algorithms

Star 10

"Frequent Mining Algorithms" is a Python library that includes frequent mining algorithms. This library contains popular algorithms used to discover frequent items and patterns in datasets. Frequent mining is widely used in various applications to uncover significant insights, such as market basket analysis, network traffic analysis, etc.

python data-science data-mining apriori frequent-itemset-mining association-rules frequent-pattern-mining apriori-algorithm fpgrowth eclat association-rule-mining eclat-algorithm apriori-algorithm-python frequent-sequence-mining hmine hmine-algorithm
  • Updated Aug 1, 2023
  • Python

rudrajit1729 / Machine-Learning-Codes-And-Templates

Star 8

Codes and templates for ML algorithms created, modified and optimized in Python and R.

feature-selection datascience feature-extraction thompson-sampling dimensionality-reduction ucb ann regression-models nlp-machine-learning kmeans-clustering apriori-algorithm hierarchical-clustering classification-algorithims parameter-tuning regression-algorithms xgboost-model kfold-cross-validation cnn-classification eclat-algorithm
  • Updated Mar 28, 2020
  • Python

RithwickNegi / Apriori-and-ECLAT-project

Star 2

We use Association rule mining for clothing style recommendation. Association rules are useful for analyzing and predicting customer behavior. In this dataset we use association rule to find the best clothing option for people. So that we can recommend other people to look for same clothing style. This pattern would help cloths designers to unde…

python jupyter-notebook apriori-algorithm datavisualization eclat-algorithm
  • Updated Jan 2, 2021
  • Jupyter Notebook

dshah98 / Machine_Learning_with_R

Star 2

Full machine learning practical with R.

natural-language-processing linear-regression artificial-neural-networks logistic-regression support-vector-machine polynomial-regression convolutional-neural-network naive-bayes-classification apriori-algorithm hierarchical-clustering knn-classification support-vector-regression decision-tree-regression k-means-clustering kernel-svm random-forest-regression multiple-linear-regression decision-tree-classification random-forest-classification eclat-algorithm
  • Updated May 23, 2021
  • R

dshah98 / Machine_Learning_with_Python

Star 2

Full machine learning practical with Python.

natural-language-processing linear-regression artificial-neural-networks logistic-regression support-vector-machine polynomial-regression convolutional-neural-network naive-bayes-classification apriori-algorithm hierarchical-clustering knn-classification support-vector-regression decision-tree-regression k-means-clustering kernel-svm random-forest-regression multiple-linear-regression decision-tree-classification random-forest-classification eclat-algorithm
  • Updated May 14, 2021
  • Jupyter Notebook

kkrusere / Market-Basket-Analysis-on-the-Online-Retail-Data

Star 1

The project dives into transaction records of an online retail business to uncover hidden relationships between products. The overall goal is a data-driven approach to enhance the customer shopping experience, improve loyalty, boost profitability, tailor marketing strategies, and optimize inventory management via strategic business decisions.

machine-learning data-engineering data-analysis market-data data-mining-algorithms kmeans-clustering apriori-algorithm market-basket-analysis sequential-pattern-mining association-rule-mining data-driven-decisions fp-growth-algorithm eclat-algorithm lift-analysis support-and-confidence-analysis
  • Updated Oct 29, 2024
  • Jupyter Notebook

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