Baran Güçlü (Baranll0)

Baranll0

Geek Repo

Company:HAVELSAN

Location:Ankara,Turkey

Home Page:https://www.kaggle.com/barangl

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Baran Güçlü's repositories

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SpringBootDockerDemo

This project is a demo project of connecting the Spring project to Docker and Jenkins.

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Dimensionality-reduction-with-LDA

Bu projede, boyut azaltma için iki popüler yöntem olan LDA ve PCA'yı karşılaştırdık.

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User-Interactive-Figma

In this application, Figma projects for the User Interactive Programming course were made.

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C-Math-Operations-With-Large-Numbers

In this program, mathematical operations are performed with large numbers that are not defined in the C programming language.

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Postfix-in-Lisp

The purpose of this application is a program that calculates arithmetic expressions written in Lisp language with Post-fix notation.

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Array-Structure-C

Bu uygulama, C programlama dilinde dizilerin ve bu dizilere karşılık gelen veri yapısının nasıl kullanılacağını gösteren bir örneği içerir.

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Facial-Expression-Recognition

This project implements a Convolutional Neural Network (CNN) for facial expression recognition using the FER2013 dataset.

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SmartFarmProject

Akıllı tarım uygulaması, tarım sektöründe verimliliği artırmak, kaynakları daha etkin kullanmak ve çiftçilere daha iyi bir yönetim imkanı sunmak amacıyla geliştirilen bir yazılım çözümüdür. Bu uygulama, modern yazılım tasarım desenleri kullanılarak Spring Framework tabanlı bir proje olarak geliştirilmiştir.

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Kredi-Verme-Durumu-Tahmini

Bu uygulamada belirli özniteliklere göre müşteriye kredi verme durumu tahmin edilmiştir. Uygulamada, Random Forest ve SVM kullanılmıştır.

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TensorFlow-IMDb-Movies

This application involves making predictions using TensorFlow on a dataset comprising IMDb's top 1000 movies.

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Student-Mental-Health

The study investigated correlations, conducted statistical tests, and applied machine learning models to predict depression levels. Delved deep into factors influencing mental health and visualized insights.

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Housing-Price-Prediction

This project focuses on prediction housing prices obtained from various sources. It includes data analysis, visualization, and machine enhancements to create predictive models.

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Exploring-Machine-Learning-Prediction-Models

Discover and apply five machine learning prediction models. Learn about Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, and Random Forest through practical applications.

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Patika-Dev-Veri-Yapilari-Projeleri

This project explores sorting algorithms and Binary Search Trees using Python, offering an interactive learning platform for fundamental computer science concepts.

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