Python code for common Machine Learning Algorithms

The R package for SVM with GPU architecture based on the GTSVM software

Statistical Analysis and predicting future TeleCommunication Data by SVR, ARIMA, LSTM, Conv_LSTM

NTHU EE6550 Machine Learning slides and my code solutions for spring semester 2017.

SVR for multidimensional labels

Based on the SVR interpolation, a new method about the time-varying channel estimation of FBMC is proposed.

Optimizers for/and sklearn compatible Machine Learning models

Predict the compensation base on the position level using the SVR machine learning model.

Using ε-Support Vector Regression (ε-SVR) for identification of Linear Parameter Varying (LPV) dynamical systems

Simple regression problem where by pre-loaded data I have shown different regression algorithm and their performance on data.

Course work of Multivariate data analysis CH5440

Photovoltaic power prediction based on weather data for my bachelor thesis

Path planning in aerial images using imitation learning

Multiple randomized ANN are being generated that is being taken from user input(total number of ANN) then we have approached one of the nature-inspired-algorithms such as DIFFERENTIAL-EVOLUTION(DE) on a soil-content-dataset to prove that it has better prediction and optimising values other than some well defined algorithms such as SUPPORT-VECTOR-REGRESSION(SVR) and MLP-REGRESSOR.

Predicting house prices using SVR

Stock Price Prediction

R codes for common Machine Learning Algorithms

Common Machine Learning Examples 💻

Ejemplo del tipo de regresión SVR (Regresión de Soporte Vectorial) en python.

Estimation System for Crops and Horticulture Production with Support Vector Regression (Internship Project)

This repo is an implementation of the research paper "A Data Mining Approach to Predict Forest Fires using Meteorological Data." by P. Cortez and A. Morais. The algorithms used are : SVR, Decision Trees, Random Forests, Simple Deep Neural Network ( Keras with Tensorflow backend)

Implementation of Different Machine learning Algorithm with Sample Datasets in Python3

Evaluation of MLP neural network for different inputs and evaluation of the results

Purpose of this project is to implement machine learning algorithms and to compare their performances on a real data set.

In this project I have analysed the forest fire on the basis of certain independent parameters in the public-ally available data-set by meteorological department.

Grade Prediction Regression using ridge, lasso, inn, and svr

Support vector machine to regression trained with kernel adatron algorithm

Bluffing Detector using Different Non-Linear ML models