Course Project of Information Retrieval.

A porting to modern g++ and C+11 of the IBM Quest dataset generator

This is projects of Data Mining

Implementation de l'algorithme de generation des items frequents en python et l'application sur une base de donnes diabetique

A project for streaming algorithms: Bloom filtering, Flajolet-Martin Algorithm, Fixed-Size Sampling

Course Notes

Code for the paper "SPEck: Mining Statistically-significant Sequential Patterns Efficiently with Exact Sampling", by Steedman Jenkins, Stefan Walzer-Goldfeld, and Matteo Riondato, appearing in the Data Mining and Knowledge Discovery Special Issue for ECML PKDD'22.

The project or work which goals to extract the opinions, emotions, attitudes of public towards different object of interest. Sentiment analysis is a form of shallow semantic analysis of texts. In the project an automatic approach that involves supervised machine learning and text mining classification algorithms are used which includes the senti…

Implementing Gaussian Naive Bayes and KNN from scratch and evaluating their performances on heart dataset

KNN algorithm in big data for the detection of anomalies using Apache Spark and Scala

FP Growth algorithm implemented using python

The dataset is about past loans. The loan_train.csv data set includes details of 346 customers whose loans are already paid off or defaulted.

Practice codes for Machine Learning, Data Mining and NLP in Python

一些数据挖掘算法

"This repository contains implementations of Boosting method, popular techniques in Model Ensembles, aimed at improving predictive performance by combining multiple models. by using titanic database."

Python Implementation of data mining algorithms(Apriori, Eclact, FP Growth ).

This contains all projects that I have done during my master degree.

RapidMiner-DataMinig

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]