Karthikaskumar's starred repositories
pytorch-forecasting
Time series forecasting with PyTorch
ML-For-Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
lightgcn_recommender_pyg
LightGCN recommender system pytorch-geometric/Jupyter notebook implementation with Python
Python_Natural_Language_Processing
This repository consists of a complete guide on natural language processing (NLP) in Python where we'll learn various techniques for implementing NLP including parsing & text processing and understand how to use NLP for text feature engineering.
recommenders
Best Practices on Recommendation Systems
handson-ml2
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
TimeSeriesLSTM
Fully coded with Google Colab.
Multivariate-time-series-prediction
Multivariate time series prediction using LSTM in keras
Multivariate-time-series-models-in-Keras
This repository contains a throughout explanation on how to create different deep learning models in Keras for multivariate (tabular) time series prediction.
Multivariate-Time-series-Analysis-using-LSTM-ARIMA
Multivariate Time series Analysis Using LSTM & ARIMA
deep-learning-ts
Modeling and forecasting time series using deep learning
Smart-Traffic
A system and method for the prediction of vehicle traffic congestion on a given roadway within a region. In particular, the computer implemented method of the present disclosure utilize real time traffic images from traffic cameras for the input of data and utilizes computer processing and machine learning to model a predictive level of congestion within a category of low congestion, medium congestion, or high congestion. By implementing machine learning in the comparison of exemplary images and administrator review, the computer processing system and method steps can predict a more efficient real-time congestion prediction over time.
pytorch_geometric_temporal
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
traffic_prediction
Traffic prediction is the task of predicting future traffic measurements (e.g. volume, speed, etc.) in a road network (graph), using historical data (timeseries).
Bigscity-LibCity
LibCity: An Open Library for Urban Spatial-temporal Data Mining
Traffic-flow-prediction
A time series task- predicting traffic flow using LSTM model
stgcn-lstm
Spatial-Temporal Graph Convolutional Neural Network with LSTM layers
Traffic-Prediction-using-SVR-and-RFR
We have used Support Vector Regression and Random Forest Regression to predict traffic or congestion.
kaggle-Traffic-Congestion-Prediction
In this project, I am trying to predict traffic congestion, based on an aggregate measure of stopping distance and waiting times, at intersections in 4 major US cities: Atlanta, Boston, Chicago & Philadelphia.
TrafficFlowPrediction
Traffic Flow Prediction with Neural Networks(SAEs、LSTM、GRU).
Traffic-congestion-predict
Traffic congestion warning system based on regression analysis and memory network
Dry_Bean_Machine_Learning
Machine Learning and Deep Learning
Data-Science-Projects
Collection of data science projects in Python
Machine-Learning-with-Iris-Dataset
Data Visualization and Machine Learning with Iris Dataset.