Using DIgSILENT, a smart-grid case study was designed for data collection, followed by feature extraction using FFT and DWT. Post-extraction, feature selection. CNN-based and extensive machine learning techniques were then applied for fault detection.

Demo on the capability of Yandex CatBoost gradient boosting classifier on a fictitious IBM HR dataset obtained from Kaggle. Data exploration, cleaning, preprocessing and model tuning are performed on the dataset

ITCS 6190 : Cloud Computing for Data Analysis project. Movie Recommendation Engine for Netflix Data with custom functions implementation and library usage.

This project gives an overview of crime time analysis in New York City . We have created Python Jupyter notebooks for spatial analysis of different crime types in the city using Pandas, Numpy, Plotly and Leaflet packages. As a second part to this analysis, we worked on ARIMA model on R for predicting the crime counts across various localities in…

Web app with interactive forecasts based on correlations

Book Recommendation System Web App

Implementation of various feature selection methods using TensorFlow library.

A package that calculates correlation between two arrays. Simple, with no dependencies

collection of utility functions for correlation analysis

A simple recommender system in python implementing: ItemKNN, UserKNN, ItemAverage, UserAverage, UserItemAverage, etc.

In this repository, four famous correlation algorithms have been implemented. Pearson, spearman, Chatterjee, and MIC correlation algorithm implemented

Hybrid RecSys, CF-based RecSys, Model-based RecSys, Content-based RecSys, Finding similar items using Jaccard similarity

Online statistics

"A set of Jupyter Notebooks on feature selection methods in Python for machine learning. It covers techniques like constant feature removal, correlation analysis, information gain, chi-square testing, univariate selection, and feature importance, with datasets included for practical application.

This repository is for Netflix movie recommendations using various content and collaborative-based methods like Word2vec, Node2vec, Sentence Transformer, MiniBatchKMeans, Cosine Similarity, Pearson's Correlation, and Singular Value Decomposition (SVD).

Compute multiple types of correlations analysis (Pearson correlation, R^2 coefficient of linear regression, Cramer's V measure of association, Distance Correlation,The Maximal Information Coefficient, Uncertainty coefficient and Predictive Power Score) in large dataframes with mixed columns classes(integer, numeric, factor and character) in para…

Develop a customer segmentation to define market strategy. The sample dataset summarizes the usage behaviour of about 9000 active credit card holders during the last 6 months.

Recommedation of movies to a user based on user rating data.

Books recommendation system by collaborative filtering and certain visualization are done on data.

Recommendation system built using multiple ML models that aim to predict users' interests based on their past behavior and preferences.