Statistical Inference via Data Science: A ModernDive into R and the Tidyverse

Python scripts with solutions for different Machine Learning tasks

Website sources for Applied Machine Learning for Tabular Data

Statistical Functions for Regression Models

A collection of 8 Applied Data Science projects.

🏠 Bangalore Property Price Prediction is a comprehensive project designed to accurately predict property prices in Bangalore. Leveraging advanced regression techniques and a dataset sourced from Kaggle, the model undergoes meticulous feature engineering, data cleaning, and parameter tuning to ensure high accuracy.

Implementação do Trabalho de conclusão de curso, com o tema definido "Aproximação da cinemática inversa de um robô manipulador didático através de algoritmos de aprendizado de máquina"

Implementing classic machine learning algorithms from scratch with NumPy.

From data preprocessing to deep learning

Some projects that I've worked on to harness the power of machine learning and data science 🙌🏻

Practice Implementation of All the Important ML Algorithms from Scratch as a Part of Master's Machine Learning Course.

In this section we will be predicting body fat using regression machine learning algorithms

Statsmodels: statistical modeling and econometrics in Python

Machine Learning Library for C++

The objective of this project is to showcase the use of Machine Learning models to answer the question of loan default prediction based on certain parameters from the German bank dataset.

This repo contains various Regression Models

Regression machine learning model to analayze past stock market history to form predictions on future stock market activity. Read more in README.

Regression models for beta distributed responses in Julia

Machine Learning | Regression Model

Using R programming This repository includes data, scripts, and documentation for analyzing and predicting crime rates in Chicago using weather data, featuring models like Linear Regression and Random Forest, and ARIMA for time series analysis.