Statistical Methods for Machine Learning project - Muffins Vs Chihuahuas

This regression model involves predicting the average life expectancy of a country's population using the dataset given.

This project analyzes tumor cell data from 550 patients using Python. It involves data cleaning, exploratory analysis, feature engineering, and machine learning to classify tumors as malignant or benign. Techniques include PCA, logistic regression, and k-fold cross-validation to ensure model accuracy and reliability.

Using k-fold cross validation to find the best classification model to predict employee attrition.

This project aims to predict heart failure outcomes by applying statistical learning algorithms. The goal is to improve the prediction accuracy through the SuperLearner algorithm.

Proyecto de investigación en ML para identificar factores genéticos en pronóstico de lesiones pre-tumorales. Aprendizaje no supervisado para discernir perfiles genéticos distintivos entre grupos de buen y mal pronóstico, mejorando detección y tratamiento temprano del cáncer.

Breast Cancer Data Analysis: Analyzes and classifies breast cancer data using a Naive Bayes classifier with preprocessing, label encoding, and k-fold cross-validation. Cars Dataset Analysis: Explores a cars dataset with data loading, statistics, and visualizations, including price distribution and correlation heatmap. Hayes-Roth Classification: C

A project about an analyzation of a statistic of damaged logging (wood) in Germany using Python.

This code about K-Fold Validation is applied to Logistic Regression.

This Project will perform linear regression on Automobiles MPG

Building a few linear models to predict 2-week ahead COVID-19 case counts.

Pytorch implementation of finetuning bert on CR dataset using k-fold cross validation.

Using machine learning, get the features and labels from the csv and perform cross fold validation and linear regression.

Repository containing Code and other materials for the Research Project on Rock Type Classification

This repository contains a basic implementation of a feed forward neural network using TensorFlow and Keras to predict the onset of diabetes in Pima Indian women based on certain diagnostic measures. The dataset used for training and evaluation is the Pima Indians Diabetes Database, which is publicly available and widely used for machine learning

The purpose of this project is to analyze some winning factors for a NBA team and predict their win rate using multiple linear regression. Different cross-validation methods were used to evaluate the model's prediction ability.

Neural Network

Data Mining project (Fall2023) involving the classification and clustering of Sars-Cov-2 gene expression RNA-seq data