imagor video thumbnail server in Go and ffmpeg C bindings

Artificial intelligence (AI, ML, DL) performance metrics implemented in Python

🔮Trying to find the best movie to watch on Netflix can be a daunting. Case Study for Recommendation System of movies in Netflix.🔧

missCompare R package - intuitive missing data imputation framework

Compare the quality between two images using RMSE, SSIM, and PSNR. (part of I3D 2018 Montage4D.com)

Self Driving Car Project 6 - Sensor Fusion(Extended Kalman Filter)

Prediction of Soybean stock prices using LSTM with data from CBOT Soybean Futures + (2015 USA Weather Avg, Max, Min by USDA-NASS-soybeans-production_bushels-2015)

🎥  Simple Python implementation of Funk SVD for MovieLens movie collaborative recommendations.

Comparing two images by using 9 metrics: VIFP, PSNR, SSIM, FSIM, RMSE, ISSM, SRE, SAM, UIQ.

Posts/Feeds recommendation engine based on content based and collaborative filtering methods

Python package to simplify plotting of common evaluation metrics for regression models. Metrics included are pearson correlation coefficient (r), coefficient of determination (r-squared), mean squared error (mse), root mean squared error(rmse), root mean squared relative error (rmsre), mean absolute error (mae), mean absolute percentage error (m…

An R package to apply affine and similarity transformations on vector layers (sp objects)

A hyperopt wrapper - simplifying hyperparameter tuning with Scikit-learn style estimators.

Building a model which can predict the number of online 'shares' an article will get based on a set of variables attached to it. (Python)

A recommendation system for Restaurants!

Leave-one-out Cross-validation for regression models

Sensor Fusion: Unscented Kalman Filter, LiDAR&RADAR

Forecasting time series data using ARIMA models. Used covariance matrix to find dependencies between stocks.

Applied Machine Learning - Book Recommender System

Develop a deep learning model capable of predicting traffic flow in urban environments. The model will utilize historical traffic data, weather conditions, and road configurations to forecast traffic patterns. This information can be invaluable for traffic management systems, helping to optimize traffic signals and reduce congestion, ultimately.