The objective is to implement different clustering methods to synthetic and real-world data and validate using external and internal validation techniques

load a dataset using Pandas and apply the following classification methods (KNN, Decision Tree, SVM, and Logistic Regression) to find the best one by accuracy evaluation methods (Jaccard, F1-score, LogLoss) for this specific dataset.

Classifying images into discrete categories based on keywords generated from the Google Cloud Vision API

The Dice Coefficient Is Scale Sensitive, Mathematical Proof.

Pipeline that learns and recognize thematics

We load a historical dataset from previous loan applications, clean the data, and apply different classification algorithms on the data.

Testing Jaccard similarity and Cosine similarity techniques to calculate the similarity between two questions.

Built a classifier to predict whether a loan case will be paid off or not. Used classification algorithms (k-Nearest Neighbour, Decision Tree, Support Vector Machine, Logistic Regression). Each result is reported with the accuracy of each classifier (Jaccard index, F1-score, LogLoass)

Document Comparison web application based on Jaccard Similarity Index. The uploaded file is compared to all previously uploaded ones. Built with Java/JSP

This project contains the KNN, SVM, Logistic Regression and Decision Tree algorithms applied to a loan data set. Model Evaluation is also presented at the end of this model.

This code generate partitions for a multilabel dataset using the Jaccard Index similarity measure. We use HCLUST with 6 linkage metrics to generate several partitions. You may build the partition with the highest coefficient. This code also provide an analysis about the partitioning.

A Google Chrome Extension that estimates the Reliability, Polarity and Subjectivity of any news article on the web. It allows you to like/dislike any article and recommends you articles based on your choices.

Fast Jaccard similarity search for abstract sets (documents, products, users, etc.) using MinHashing and Locality Sensitve Hashing

Machine Learning with Python

Using Spark In Python For Movie Similarities With Jaccard Index

Different clustering and clustering metrics are implemented in this repository

build a classifier to predict whether a loan case will be paid off or not. in loan applications, clean the data, and apply different classification algorithm on the data. use the following algorithms to build your models: k-Nearest Neighbour Decision Tree Support Vector Machine Logistic Regression The results is reported as the accuracy of each …

A platform for both students and instructors to browse courses in the MOOC world easily. The platform features a recommender system that predicts courses of users preference from past courses, a Student-Instructor Course enrollment and Real-Time Discussion Forum Systems.

This code is part of my doctoral research. The aim is to generate partitions from the Jaccard index for multilabel classification.