metehanayhan Goto Github PK

adspend-impact-analysis

This project aims to analyze the impact of TV, Radio, and Newspaper advertising expenditures on sales. Using machine learning techniques, a multiple linear regression model has been developed to determine how each advertising channel affects sales.

ai-works

This repository contains the code I wrote while working on data science and artificial intelligence.

house-prices-advanced-regression-techniques

Predict house prices in Ames, Iowa using machine learning models like Gradient Boosting and Random Forest. This project involves data preprocessing, feature engineering, and model evaluation with RMSE.

laptop-price-prediction

This project aims to predict laptop prices based on various features and configurations using machine learning algorithms. The goal is to identify the most influential features on laptop pricing and develop a model that can make accurate predictions.

loan-prediction-model

A project predicting loan defaults using machine learning techniques. The model analyzes customer data to assess the likelihood of loan repayment and identifies key factors influencing loan approval decisions.

metehanayhan

Config files for my GitHub profile.

metehan_ayhan_turkcell_proje

predictive-maintenance-project

A machine learning project focused on developing a predictive maintenance model for a delivery company. The model aims to predict equipment failures in advance, enabling proactive maintenance and reducing operational downtime.

school-projects

This repository contains my school projects, showcasing various programming assignments and concepts I've learned, including data structures, algorithms, and software development practices.

sentiment-analysis-with-nlp

A Natural Language Processing (NLP) and classification project that analyzes customer reviews from platforms like Yelp, Foursquare, and Twitter to enhance customer satisfaction for Bacchanal Buffet.

titanic-survival-prediction-with-ml

A machine learning project that predicts the survival of passengers aboard the Titanic using various classification algorithms. The project leverages data preprocessing, feature engineering, and model evaluation to optimize predictions. Developed as part of a Kaggle competition.