Model Training and Tuning with Multiple Datasets
Data Science Model Training and Tuning
Overview: Welcome to the Data Science Model Training and Tuning project! This repository contains an R Notebook that demonstrates the process of training and tuning statistical models using various datasets. From exploratory data analysis to model evaluation, this notebook covers a wide range of topics in data science.
Features: Exploratory Data Analysis (EDA) using visualizations Training regression and classification models with datasets such as Iris, Boston Housing, and Titanic Hyperparameter tuning using techniques like grid search Model performance evaluation using metrics like AUC-ROC and Bland-Altman plots Comparison of multiple models using resampling techniques and graphical visualizations.
Requirements: To run the R Notebook and replicate the analysis in this project, you will need:
R (version 3.5 or higher)
RStudio (optional but recommended)
Usage
Clone this repository to your local machine
Open the model_training_and_tuning.Rmd file in RStudio.
Run each code chunk sequentially to reproduce the analysis.
License This project is licensed under the MIT License. Feel free to use and modify the code for your own purposes. See the LICENSE file for more details.
Acknowledgements This project was inspired by the book "Applied Predictive Modeling" and utilizes datasets from the mlbench package.