Using Machine Learning Algorithms for Regression Analysis to predict the sales pattern and Using Data Analysis and Data Visualizations to Support it.
-
Updated
May 13, 2019 - Jupyter Notebook
Using Machine Learning Algorithms for Regression Analysis to predict the sales pattern and Using Data Analysis and Data Visualizations to Support it.
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.
Nature-inspired algorithms for hyper-parameter tuning of Scikit-Learn models.
DataFrame support for scikit-learn.
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.
Hyper-parameter tuning of classification model with Mealpy
This is Project which contains Data Visualization, EDA, Machine Learning Modelling for Checking the Sentiments.
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.
Text classification with Machine Learning and Mealpy
Hyper-parameter tuning of Time series forecasting models with Mealpy
Hyper-Parameter Optimisation experiment as part of my undergraduate dissertation (2019)
Predicting the Contraceptive Method Choice of a Woman Based on Demographic and Socio-economic Characteristics - The objective of this study is to to predict the contraceptive methods (no use, long-term methods, or short-term methods) of a woman based on her demographic and socio-economic characteristics. A data-set of 1473 married women with the…
Combined hyper-parameter optimization and feature selection for machine learning models using micro genetic algorithms
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
Using Facebook Adaptive Experimentation platform to tune random forest regressors using docker
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…
Modeling of strength of high performance concrete using Machine Learning
The used cars price is predicted using various features - Decision Tree & Random Forest
A simple python interface for running multiple parallel instances of a python program (e.g. gridsearch).
Examples of parameter tuning via DrOpt.
Add a description, image, and links to the hyper-parameter-tuning topic page so that developers can more easily learn about it.
To associate your repository with the hyper-parameter-tuning topic, visit your repo's landing page and select "manage topics."