hyper-parameter-tuning

DataFrame support for scikit-learn.

Using Machine Learning Algorithms for Regression Analysis to predict the sales pattern and Using Data Analysis and Data Visualizations to Support it.

Nature-inspired algorithms for hyper-parameter tuning of Scikit-Learn models.

In this data set we have perform classification or clustering and predict the intention of the Online Customers Purchasing Intention. The data set was formed so that each session would belong to a different user in a 1-year period to avoid any tendency to a specific campaign, special day, user profile, or period.

In this data set, We have to predict the patients who are most likely to suffer from cervical cancer using Machine Learning algorithms for Classifications, Visualizations and Analysis.

This is Data set to Classify the Benign and Malignant cells in the given data set using the description about the cells in the form of columnar attributes. There are Visualizations and Analysis for Support.

This is Project which contains Data Visualization, EDA, Machine Learning Modelling for Checking the Sentiments.

Convenient classes for optimizing Hyper-parameters, using Random search, Spearmint and SigOpt

Graded assignments of all the courses that are being offered in Coursera Deep Learning Specialization by DeepLearning.AI. (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Network (v) Squence Model

Text classification with Machine Learning and Mealpy

Hyper-parameter tuning of Time series forecasting models with Mealpy

Hyper-parameter tuning of classification model with Mealpy

Combined hyper-parameter optimization and feature selection for machine learning models using micro genetic algorithms

A gradient free optimization routine which combines Particle Swarm Optimization with a local optimization for each particle

Surrogate adaptive randomized search for hyper-parameters tuning in sklearn.

Efficient and Scalable Batch Bayesian Optimization Using K-Means

The data used in this analysis is an Online Shoppers Purchasing Intention data set provided on the UC Irvine’s Machine Learning Repository. The primary purpose of the data set is to predict the purchasing intentions of a visitor to this particular store’s website. The data set was formed so that each session would belong to a different user in a 1-year period to avoid any tendency to a specific campaign, special day, user profile, or period.

Hyper-Parameter Optimisation experiment as part of my undergraduate dissertation (2019)

A simple python interface for running multiple parallel instances of a python program (e.g. gridsearch).

Using Facebook Adaptive Experimentation platform to tune random forest regressors using docker