Basic Introduction to Data Science with RPractical Machine Learning with the mtcars Dataset in R

Put your data science skills into practice by working on machine learning projects using the classic `mtcars` dataset in R. This course provides hands-on experience with end-to-end solutions, from data preprocessing to model evaluation, ensuring you are prepared for real-world tasks.

Adapting Data Preprocessing Routine

Fix the Data Preprocessing Bug

Data Exploration with mtcars

Preprocessing with Mean Calculation

Exploring the mtcars Dataset

Change the Data Split Ratio

Find and Fix the Errors

Fill in the Missing Code

Splitting and Scaling in R

Split and Scale Your Data

Change Logistic Model Prediction

Fix Bug in Logistic Model

Train a Binary Classifier Model

Train a New Logistic Model

Multiclass Logistic Regression in R

Switch to Random Forest Model

Fix the Prediction Code

Making Predictions and Evaluation

Evaluating Model Predictions

Write Code to Do Predictions and Evaluate

Plot Theme Comparison

Fix the Logistic Regression Plot

Add Missing Parts for Visualization

Visualize Logistic Regression Coefficients

Visualize Variable Importance

Modify Cross-Validation Folds

Fix Cross-Validation Code

Cross-Validate with Seven Folds

Cross-Validation From Scratch