-machine-learning-on-streaming-data-using-kafka-and-docker

I have completed my first project that machine learning on streaming data using Kafka and Docker. You can check-up my GitHub repository for codes.

120-yillik-olimpiyat-verileri

article-meeting

auto_ts

Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Now updated with Dask to handle millions of rows.

binancedatapipelineinaws

brain-tumor-detection-with-vgg16

building-data-science-applications-with-fastapi

Building Data Science Applications with FastAPI, Published by Packt

cardiovascular_risk_prediction_with_pso

As a result, it performed as well as other models. The worst disadvantage is the time! It takes almost four hours to run. Therefore, this algorithm still has a long way to go. However, it provides an alternative to standard algorithms.

cart-analysis-for-stroke-prediction

CART analysis¶ As computing power and statistical insight has grown, increasingly complex and detailed regression techniques have emerged to analyze data. While this expanding set of techniques has proved beneficial in properly modeling certain data, it has also increased the burden on statistical practitioners in choosing appropriate techniques. Arguably an even heavier burden has been placed on non-statistician health practitioners – in university, government, and private sectors – where statistical software allows for immediate implementation of complex regression techniques without interpretation or guidance. In response to this growing complexity, a simple tree system, Classification and Regression Tree (CART) analysis, has become increasingly popular, and is particularly valuable in multidisciplinary fields.

catboost_shap_smote

Of all the applications of artificial intelligence, diagnosing any disease using a "black box" is always going to be a hard explanation. Those who will use the application will want to know how the model decides on the treatment conditions or following-up conditions according to the model result. Or data provider clinicians will want the model with the highest performance in their project. This dataset classified patients according to sacral position properties. I investigated using the below techniques for the best result and explainable machine learning model; Balancing unbalanced medical data Creating models with CatBoost Classifier Finding the most optimized parameters by Grid Search with the Optuna library Artificial intelligence algorithms described as Black Box are actually explainable SHAP library tutorial Combined use of RFECV and SHAP library for Feature Selection Comparison of all applied models to each other

chat-gpt-stt

chatgpt-lambda-streamlit-docker

the ChatGPT model, the AWS Lambda function, and the Streamlit interface with Docker.

clinicalbert-deep-learning--predicting-hospital-readmission-using-transformer

Blog post on Medium

d-hondt-metoduyla-milletvekili-da-l-m-

Gönül isterdi ki siyasetsiz bir hayat yaşamak

deprem-doktoru

depremdoktor.site adresinde bulunan ilaç hesaplama ve demprem ilişkili kliniklerin yönetimini içerir

e-mail_finder_for_junior_editors

This code is working effeciently on pubmed for PubMed display option.

ecg-pipeline

Our end-to-end pipeline is designed to flexibly process data from different recording machinery and to read data in PDF format as well as data from native digital devices delivered in XML.

echo-segmentation

email-finder-pubmed-for-editors

finding-best-categorization-with-pycaret

I am really curious that, how I must create categorical features from numeric features. The most commonly used method is separating with the same intervals and stratification with quantiles. But my experience in medicine showed that this stratification threshold is wrongly chosen. For example, I have already dropped some values in the "BMI" feature that are bigger than 60 and smaller than 14. But some notebooks include them and are replaced them with some values. But other hand, when I used medical categorization guides also doesn't result in good model performance.

fuzzy_anomaly_detection

A HYBRID APPROACH TO ANOMALY DETECTION USING FUZZY LOGIC TUNED WITH EVOLUTIONARY ALGORITHMS

generative_ai_with_langchain

Build large language model (LLM) apps with Python, ChatGPT and other models. This is the companion repository for the book on generative AI with LangChain.

genetic-algorithm

In this repository, I will present a tutorial of Genetic Algorithm.

getting-things-done-with-pytorch

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BER

heart_failure_prediction_with_genetic_algorithm

My primary goal in this notebook, is we have a lot of algorithms for solutions, just know what you are searching for

is-boster-shot-necessary-time_series

I am trying to forecast COVID disease progress with time series algorithms

kaplan_meier_survival_analysis_with_python

lsc-webpage

My first web-page project

mango

Parallel Hyperparameter Tuning in Python

mdxapp

A ChatGPT-powered Medical Diagnosis Assistant Experience