#Cross-validation
Showing 25 of 25 repositories tagged #cross-validation, ranked by stars
Hyperparameter optimization and feature selection for scikit-learn using evolutionary algorithms. A modern alternative to GridSearchCV and RandomizedSearchCV.
It is a Natural Language Processing Problem where Sentiment Analysis is done by Classifying the Positive tweets from negative tweets by machine learning models for classification, text mining, text analysis, data analysis and data visualization
Time Series Cross-Validation -- an extension for scikit-learn
State-of-the art Automated Machine Learning python library for Tabular Data
A Portfolio of my Data Science Projects
A library that unifies the API for most commonly used libraries and modeling techniques for time-series forecasting in the Python ecosystem.
Customers in the telecom industry can choose from a variety of service providers and actively switch from one to the next. With the help of ML classification algorithms, we are going to predict the Churn.
Hyperparameter tuning for machine learning models using a distributed genetic algorithm
Spells for everyday living, also a book -- Models Demystified -- now available!
Spatial Modelling for Data Scientists
โ๐ผ๐ This one stop project is a complete COVID-19 detection package comprising of 3 tasks: โข Task 1 --> COVID-19 Classification โข Task 2 --> COVID-19 Infection Segmentation โข Task 3 --> Lung Segmentation
DataFrame support for scikit-learn.
pytorch implementation of paper https://www.frontiersin.org/articles/10.3389/fcomp.2020.00035/full
Use the famous CIFAR-10 dataset to train a multi-layer neural network to recognize images of cats, dogs, and other things.
Spatial cross-validation in Python.
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Time based splits for cross validation
Handwritten digit recognizer using a feed-forward neural network and the MNIST dataset of 70,000 human-labeled handwritten digits.
Using Machine Learning Algorithms for Regression Analysis to predict the sales pattern and Using Data Analysis and Data Visualizations to Support it.
Engaged in research to help improve to boost text sentiment analysis using facial features from video using machine learning.
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.
Resampling Tools for Time Series Forecasting with Modeltime
A tool for performing cross-validation with panel data
Signal diagnostics, statistical validation, and backtest evaluation for quantitative trading workflows.
scikit-learn-compatible time-series cross-validation: purging, embargo, combinatorial purged CV, and deflated Sharpe ratios