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10-model-tuning-and-ensembling-with-visualisations

This study makes data visualisations of Titanic data and using that data predicts which passengers would survive from the Titanic disaster by means of machine learning modelling techniques. It is going present a full machine learning work flow, use 10 Machine Learning algorithms, tune their parameters and ensemble the best n (e.g. 6) of them using their accuracy scores for the validation set.

10-model-tuning-ensembling-first-n-automatically

Predicts survival probabilities through model tuning and ensembling. Just you need to input how many models you want to ensemble out of 10 classification models. In addition to the input opportunity, the differences of this script from the notebook are being more compact,fast and focusing on the result without much visualization,

adanzyedocker

A'dan Z'ye Docker Eğitimi

automationproject01

intellij

automationreview

backendtestingeu7

bash_docker_practice

git bash docker practices

bleucrm-group-project

bleucrm_employee

bookit-qa3

churn-prediction-of_bank_customers-by-selecting-from-11-tuned-models

Predicts the exiting probabability of the bank customers by selecting from 11 tuned ML models

effective_jupyterlab

Using JupyterLab Effectively (JupyterLab’ı etkin bir şekilde kullanma)

eu7-bookit-project

eu7_optionaltaksautomation

Optional Task from Oscar

eu7cucumberselenium

eu7testngselenium

face-mask-detection

In this, I am attaching my code for building a CNN model to detect if a person is wearing face mask or not using the webcam of their PC.

fast-style-transfer

TensorFlow CNN for fast style transfer ⚡🖥🎨🖼

github-profile-readme-generator

:rocket: Generate github profile README easily with latest add-ons like visitors count, github stats, etc using minimal UI.

gitreview

gitweek2reviewprojectberkan

group15practice

groupproject

INITIAL PROJECT

helper-functions

Helper functions I use frequently but always forget (smh)

heroku-churn-ensembled-model

Online deployment of Churn-prediction-of_bank_customers-by-selecting-from-11-tuned-models project

home-credit-default-risk-production-level

Predicts the default risk of the Home credit's customers using LGBM model

iee-cis-fraud-detection-using-3-models

We predicted fraud using XGBoost, LGBM and Catboot in IEE-CIS data set.

javaprogramming

memory-reduction-function

Reduces RAM utilization by manipulating data types properly towards low size data types

notebook-as-pdf

Save Jupyter Notebooks as PDF