"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 🚀"

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

Machine learning Applications

This repository explores fundamental ML algorithms built from scratch, enhancing understanding of the internal mathematical workings of these algorithms.

Benchmark results repository service

Interpretable Configurational Regression: An R Package

The ultimate business objective is to leverage the regression model to provide accurate predictions of the closing price of AMRN stock, enabling stakeholders to make well-informed investment decisions, manage risks effectively, optimize portfolios, Early warning systems to alert any fraud cases and align investment strategies with financial goals.

Understanding and predicting global happiness following The Great Recession and the COVID-19 pandemic.

Python regression and probabilities snippets

Regression Error Characteristic (REC) Curves in Python

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

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.

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.