This code evaluates the performance of a logistic regression model on age prediction using various features to predict a binary target variable, calculating metrics to determine the performance. It evaluates the comparison, identifies favorable features, and visualizes the ROC-AUC curve to determine the best model performance.

Linear regression models are used to predict football player attacking stats based on attributes like finishing and passing, with the model trained, evaluated, and applied for predictions. Multiple features improve accuracy, and performance is assessed using metrics like MSE and R-squared.

A movie recommendation system on MovieLens 25M dataset using Python and Apache Spark

Compute a moving root mean squared error (RMSE) incrementally.

Compute the root mean squared error (RMSE) incrementally.

imagor video thumbnail server in Go and ffmpeg C bindings

This project aims to enhance the accuracy and efficiency of stock market predictions by employing a sophisticated machine learning methodology. This project leverages the power of PySpark, a robust framework for distributed data processing, to handle large datasets and perform complex computations.

Exploratory data analyzing of Global Financial Database and loan borrowing prediction using PySpark

Car insurance prediction using Logistic Regression and the Naive Bayes

House sale price prediction using Linear Regression

Advanced Image Enhancement and Data Recovery: Superresolution Techniques and Missing Data Handling

This Project will perform linear regression on Automobiles MPG

Artificial intelligence (AI, ML, DL) performance metrics implemented in Python

Comparing two images by using 9 metrics: VIFP, PSNR, SSIM, FSIM, RMSE, ISSM, SRE, SAM, UIQ.

[DACON Competition] experience Jeju specialities

experience Jeju specialities

This machine learning project focused on predicting food delivery times. The code emphasizes essential tasks such as data cleaning, feature engineering, categorical feature encoding, data splitting, and standardization to establish a solid foundation for building a robust predictive model.

Optimizing lighting distribution across regions by adjusting lamp powers.