EDA-REGRESSION-CLASSIFICATION-WITH-BALCK-FRIDAY-DATASET
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Updated
May 28, 2019 - Jupyter Notebook
EDA-REGRESSION-CLASSIFICATION-WITH-BALCK-FRIDAY-DATASET
Badanie metod skalowania w algorytmach genetycznych
In this project I intend to predict customer churn on bank data.
In this ML, I predict the price of houses and compare accuracy using multiple models such as random forest regressor, MLP regressor, linear regression, and XGBoost.
An application that demonstrates the ability to change the scaling of the parent window through the child modal window. Written in C# WPF.
A machine learning model that attempts to predict whether a loan from LendingClub will become high risk or not.
A Java library for classical test theory, item response theory, factor analysis, and other measurement techniques. It provide tools commonly used in psychometrics and operational testing programs.
I have performed OCR for handwritten Hindi characters using dense neural network. In preprocessing I have applied scaling and PCA.
Neat_FrontEnd
[archived] Lesson files in 3L Scaling Methods for Social Science: Estimating Patterns and Preferences in Surveys and Behavior (2020 ESSEX SUMMER SCHOOL), taught by Royce Carroll
in this project, a model is designed to predict the number of vaccination against corona virus.
contains the basic structure that a model serving application should have. This implementation is based on the Ray Serve framework.
My Monte-Carlo numerical simulations of competing dynamics Ising model
Optimization algorithms and heuristics
Estimation of vital status of patients with ovarian cancer using Machine Learning models
learning python day 4
📗 This repository provides an in-depth exploration of the predictive linear regression model tailored for Jamboree Institute students' data, with the goal of assisting their admission to international colleges. The analysis encompasses the application of Ridge, Lasso, and ElasticNet regressions to enhance predictive accuracy and robustness.
build a models that predicts whether an individual makes over $50,000 per year.
Este proyecto consiste en clasificar un celular en el intervalo de precio correspondiente utilizando algoritmos de machine learning.
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