Association Rule Mining Using FP-Growth & ANN Techniques
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Updated
Aug 9, 2025 - Jupyter Notebook
Association Rule Mining Using FP-Growth & ANN Techniques
The project's objective is to harness a HR Analytics dataset. With predictive proccess I tried to equip HR management with actionable insights, enabling them to proactively address attrition issues and implement targeted retention strategies.
Practice codes for Machine Learning, Data Mining and NLP in Python
Python Implementation of data mining algorithms(Apriori, Eclact, FP Growth ).
Course Code: CS626, MCS Batch-2019 (Final Year) Evening
This contains all projects that I have done during my master degree.
This Repository contains the Intermediate level of programing code that are used in Data mining, e.t.c for Understanding of Algorithms and various other tasks.
Datamining concepts
Market basket analysis is a technique used mostly by retailers to identify which products clients purchase together most frequently. This involves analyzing point of sale (POS) transaction data to identify the correlations between different items according to their co-occurrence in the data.
A Data Mining project which focuses on the comparison between different un-supervised clustering algorithms on geographical data
A data mining project using the Apriori algorithm for Market Basket Analysis. Association rules were generated and interpreted across various parameter settings to discover purchasing patterns in retail transaction data.
some training, learning and TD/TP ressources
Used clustering algorithms such as K-Means, Fuzzy C-Means, and Density-Based Algorithms like DBScan to cluster three datasets and reported result of the best algorithm after 200 random starting points.[part of my data mining course]
Desarrollo de algoritmos de Data Mining para encontrar reglas de asociación
Data Mining Model For Detection of Fraudulent Behaviour
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