turkalpmd

-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.

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

chatgpt-lambda-streamlit-docker

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

email-finder-pubmed-for-editors

MDxApp

A ChatGPT-powered Medical Diagnosis Assistant Experience

OpenAI-medical-text-to-dataframe

Medical Text Extraction

stream-medical-data

turkalpmd/stream-medical-data

turkalpmd/-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.

turkalpmd/brain-tumor-detection-with-vgg16

turkalpmd/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

turkalpmd/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

turkalpmd/is-boster-shot-necessary-time_series

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

turkalpmd/lsc-webpage

My first web-page project

turkalpmd/time-series-with-pycaret

turkalpmd/time_series

In this section, you can see my work on time series.

turkalpmd/Zscore_hacettepe

turkalpmd/120-yillik-olimpiyat-verileri

turkalpmd/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.

turkalpmd/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.

turkalpmd/e-mail_finder_for_junior_editors

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

turkalpmd/ClinicalBERT-Deep-Learning--Predicting-Hospital-Readmission-Using-Transformer

Blog post on Medium

turkalpmd/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.

turkalpmd/fuzzy_anomaly_detection

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

turkalpmd/Genetic-Algorithm

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

turkalpmd/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

turkalpmd/Kaplan_Meier_Survival_Analysis_with_Python

turkalpmd/Medical_mnist_with_vgg16_transfer_learning_100-_accuracy

What is Transfer Learning? Transfer learning is a machine learning technique where a model trained on one task is re-purposed on a second related task. This form of transfer learning used in deep learning is called inductive transfer. This is where the scope of possible models (model bias) is narrowed in a beneficial way by using a model fit on a different but related task.

turkalpmd/Particles_swarm_optimisation_benchmark

The Particle swarm optimization (PSO) is one of the most popular metaheuristic methods proposed by (Kennedy and Eberhart 1995). The PSO emulates the social behavior of birds to search the food sources in which these birds share the information between them (the position of each one and the nearest particle to the source of food). In the PSO method, the position xi of each particle represents the solution of the given problem and the best solution represents the source of solution. In the PSO method, each particle has its own memory to save the previous best position reached by the particle and the global best position of the entire population (that belongs to it).

turkalpmd/PyCaret-regressor-for-integrating-missing-values

Today I will implement very obsessively handlig to missing values.

turkalpmd/pytorch_n_fastai_examples

My toy examples of pytorch and fastai

turkalpmd/Turkce-isim-yaratma-kilavuzu

Random Türkçe isim yaratma kılavuzu

turkalpmd/Tutorial-of-PyGAD-GANN-module

In this repository, I will present an tutorial content where we will review the GANN module of the PyGAD library.