A lightweight product recommendation system (Item Based Collaborative Filtering) developed in Haskell.

This repository contains various classification, clustering and data analysis code.

This repository contains various assignments that I have done as a part of the Machine Learning course.

Compression algorithm based kernel perceptron using Jaccard's similitary

Function for calculating the Jaccard index and Jaccard distance for binary attributes

By clustering similar tweets together, we can generate a more concise and organized representation of the raw tweets, which will be very useful for many Twitter-based applications (e.g., truth discovery, trend analysis, search ranking, etc.)

Tweets clustering K-means

String distances in rust

Locality sensitive hashing based plagiarism checker

Given a directed social graph, have to predict missing links to recommend users.

Clustering similar tweets using K-means clustering algorithm and Jaccard distance metric

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

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

Created three different spelling recommenders, that each take a list of misspelled words and recommends a correctly spelled word for every word in the list. Each spelling recommender uses different Jaccard distance metrics. For every misspelled word, the recommender find the word in correct spellings that has the shortest distance, and starts wi…

Clustering Amazon review data around 6M users using Kmeans and Dbscan algorithm.

How Far Would You Go for Italian Mozzarella? Exploring the impact of product cost and purchase frequency on distance traveled by Italian consumers

A string metric that measures proximity between 2 words. The metric calculation is a formula that utilizes 3 existing String metric algorithms: Jaccard Distance, Edit Distance and Longest Common Substring Distance.

A graph mining problem where the task was to predict a link between the given nodes. Engineered different features like Jaccard Distance, Cosine-Similarity, Shortest Path, Page Rank, Adar Index, HITS score and Kartz Centrality. Finally built non-linear models to get the final F1 score as 0.92.

Set of tasks solved in Big Data Algorithms course