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Hyperactive

SimonBlanke / Hyperactive

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An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
python metadata data-science machine-learning deep-learning optimization scikit-learn parallel-computing keras pytorch artificial-intelligence xgboost hyperparameter-optimization feature-engineering bayesian-optimization automated-machine-learning parameter-tuning neural-architecture-search meta-learning meta-heuristics
  • Updated Aug 20, 2021
  • Python

nccr-itmo / FEDOT

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Forecasting smoothed time series

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Dreamlone
Dreamlone commented Jul 1, 2021

A possible improvement of the time series smoothing operation is proposed. Below is example of applying smoothed operation
image

Currently, the implementation of smoothing allows modifying only the "features" time series (features field in InputData/OutputData). But it does not change the

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kennedyCzar / STOCK-RETURN-PREDICTION-USING-KNN-SVM-GUASSIAN-PROCESS-ADABOOST-TREE-REGRESSION-AND-QDA

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Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks
pipeline random-forest prediction stock logistic-regression predictive-analysis stocks adaboost predictive-modeling algorithmic-trading decision-tree svm-classifier quadratic-discriminant-analysis parameter-tuning guassian-processes gridsearchcv knn-classifier
  • Updated Dec 3, 2020
  • Jupyter Notebook

rodrigo-arenas / Sklearn-genetic-opt

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NikhilaThota / CapstoneProject_House_Prices_Prediction

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Understand the relationships between various features in relation with the sale price of a house using exploratory data analysis and statistical analysis. Applied ML algorithms such as Multiple Linear Regression, Ridge Regression and Lasso Regression in combination with cross validation. Performed parameter tuning, compared the test scores and suggested a best model to predict the final sale price of a house. Seaborn is used to plot graphs and scikit learn package is used for statistical analysis.
python machine-learning correlation linear-regression cross-validation data-visualization data-extraction data-analysis regularization standardization datawrangling predictive-modeling ridge-regression data-exploration k-fold lasso-regression encoding-library parameter-tuning root-mean-squared-error-metric regression-analysis
  • Updated Jan 19, 2018
  • Jupyter Notebook

Cheejyg / Genetic-Algorithms-for-Swarm-Parameter-Tuning

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Swarming behaviour is based on aggregation of simple drones exhibiting basic instinctive reactions to stimuli. However, to achieve overall balanced/interesting behaviour the relative importance of these instincts, as well their internal parameters, must be tuned. In this project, you will learn how to apply Genetic Programming as means of such tuning, and attempt to achieve a series of non-trivial swarm-level behaviours.
genetics fyp tuning genetic-programming swarm genetic-algorithms ntu tuning-parameters final-year-project swarm-intelligence parameter-tuning nanyang-technological-university
  • Updated Dec 2, 2019
  • Python

mansipatel2508 / Yelp-Review-Stars-Prediction-with-Machine-Learning

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The project has text vectorization, handling big data with merging and cleaning the text and getting the required columns while boosting the performance by feature extraction and parameter tuning for NN, compares the Performances through applied different models treating the problem as classification and regression both.
machine-learning neural-network tensorflow svm linear-regression sklearn machine-learning-algorithms regression feature-extraction classification logistic-regression tf-idf text-processing data-preprocessing data-cleaning multinomial-naive-bayes parameter-tuning knn-classification text-vectorization machinelearning-python
  • Updated Aug 9, 2019
  • Jupyter Notebook

sharmaroshan / Christiano-Ronaldo---Goal-Prediction-Top-40-

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It is a Problem Which I got During the ZS Data Science Challenge From Interview Bit Hiring Challenge Where I secured a 40th Rank out of 10,000 Students across India. It is a Dataset which requires Intensive Cleaning and Processing. Here I have Performed Classification Using Random Forest Classifier and Used Hyper Tuning of the Parameters to achieve the Accuracy. I got a very Satisfiable Accuracy from the Model in both the Training and Testing Sets.
python machine-learning random-forest jupyter-notebook data-visualization classification data-analysis profiling data-cleaning parameter-tuning target-encoding predicting-missing-values
  • Updated Aug 19, 2019
  • Jupyter Notebook

rudrajit1729 / Machine-Learning-Codes-And-Templates

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Codes and templates for ML algorithms created, modified and optimized in Python and R.
feature-selection datascience feature-extraction thompson-sampling dimensionality-reduction ucb ann regression-models nlp-machine-learning kmeans-clustering apriori-algorithm hierarchical-clustering classification-algorithims parameter-tuning regression-algorithms xgboost-model kfold-cross-validation cnn-classification eclat-algorithm
  • Updated Mar 28, 2020
  • Python

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