Benchmark results repository service

Fast estimation of generalized linear models with high dimensional categorical variables in Julia

Java·Applied·Geodesy·3D - Least-Squares Adjustment Software for Geodetic Sciences

My portfolio website regarding data science projects. Some visualization and analysis projects reflect work for PITAPOLICY clients.

Personal notes during reading "An R Companion to Applied Regression"

Predicting the 2024 Mexican presidential elections

This repository explores the activation patterns of A2 noradrenergic neurons in fear-conditioned rats, using statistical analyses like t-tests and linear regression in R. It focuses on the differences in dopamine β-hydroxylase (DbH) neuron activation between various environmental conditions.

Projects from the Learn SQL certification from Codecademy.

This project is a Jupyter Notebook that analyzes how a regression model can be tuned to predict the stock market prices of Tesla (TSLA). The objective was to create a prediction algorithm to forecast the closing price of Tesla stock on a specific date.

Leveraging sentiment analysis and data augmentation to recreate recipe scoring algorithm with sparse data. Used MLPs and Gradient Boosting Regressors to compare regression metrics such as RMSE and MSE between raw data and raw data in conjunction with augmented data.

A collection of Python coded analysis and automation of risk mitigation strategies in portfolio management.

A tool for visualizing the coefficients of various regression models, taking into account empirical data distributions.

Bitcoin (BTC) Price movement and Volume Change relationship with the reddit user opinions

Python scripts with solutions for different Machine Learning tasks

Proyek ini bertujuan untuk melakukan klasifikasi menggunakan dua model pembelajaran mesin: Naive Bayes dan Regresi Logistik. Hasil prediksi dan akurasi dari kedua model akan dibandingkan dan divisualisasikan.

"Laptop Price Predictor 🎮🔍 - ML-driven app using Random Forest Regressor. Predict prices based on features like RAM, memory, and processor. Achieved R2 score of 90%. Built with Scikit-learn, Pandas, and Numpy. #MachineLearning #DataScience #Streamlit 🚀"

🐶 Loyal GitHub action that sniffs out any follow-up and revert commit of your cherry-picked commit

Performed rigorous preprocessing, and data cleaning, and conducted exploratory data analysis to identify trends, patterns, and outliers, leading to valuable insights. Employed various statistical methods concepts to get insights about the data for prediction.

In this repository, software applications in simulation and visualization for various applications are presented with interesting examples.