an attempt at making neural networks from scratch.

applying Linear Regression (Simple and Multiple), Polynomial Regression, and Logistic Regression on real-world datasets.

Machine Learning project for predicting Car prices using Linear, Ridge, and Lasso Regression with performance evaluation (MSE, RMSE, MAE, R²) .

Bu proje, çoklu doğrusal regresyon kullanılarak balık cins, uzunluk(1,2,3), yükseklik ve genişlik bilgilerine göre ağırlık tahmini yapmaktadır. Veri ön işleme, Backward Elimination ile öznitelik seçimi, model değerlendirme ve Flask tabanlı web arayüzü uygulanmıştır.

A Regression project aiming to forecast the expenses of individuals within an insurance company where we could then decide whether to charge them a premium package or not.

ML-powered Streamlit app to predict student academic performance using regression models

This project explores the prediction of energy consumption using machine learning, with a focus on comparing Linear Regression and Random Forest models. It emphasizes understanding evaluation metrics (RMSE and R²) and examines how feature engineering—such as encoding categorical variables and extracting temporal features—affects model performance.

End-to-end ML project predicting California house prices using tuned RandomForest (CV RMSE: $48,713) with FastAPI REST endpoint and EDA notebook.

House price prediction using Decision Tree Regression with data analysis and machine learning techniques.

Regression for Coffee dataset in the US market using R and Python

This project is dedicated to predicting real estate prices in order to increase the number of transactions and simplify the work of a real estate agency using various regression models on real data.

Uncover customer churn patterns in telecom data. Utilizing logistic regression, we predict churn and assess model performance. The README guides users through dataset overview and steps. Find detailed insights in the accompanying presentation.