Statistical Learning in R

Set of mini R problems that focuses statsitical methods and regression analysis.

Table of Contents

🍩 Delicious treats with plot3d

📈 Regression models and all the goodies

🏠 PCA with using the almanac

🐧 Clusters with penguin dataset

🏘 K Nearest Neighbor

Creating Delicious Treats with Plot3d

• Test with mutiple dataset and use plot3d() to build the dessert
• Use covariance matrix diag() in the context of linear algebra in generating data.
• Use MASS package mvnorm() to generate multivariate normal distribution points to form the "cloud".
• View the project here ✔

Regression Models and All The Goodies

• Examine the predictor and response variables by building a linear regression model using lm() and plot() .
• Check influential and leverage points with rstandard(), lm.influence(), and cook.distance().
• Build a standard deviation table using dplyr and Scale-Location model to check for variance changes.
• Transform the data to further improve the model and build a prediction table using predict.lm().
• Explain the results in confint(), summary(), anova() and diagnostic plots.
• View the project here ✔

Principal Components with Almanac Data

• Editing...

How Many Clusters of Penguin Cuteness

• Editing...

K Nearest Neighbor

• Using KNN as an alternative approach to simple linear regression.
• Predict result by choosing differnet K values.
• Write a loop to run KNN method on every observation.
• View the KNN statistic model here ✔
• Using KNN in machine learning to predict flow patterns.
• Hyperparameter tuning the model.
• View the KNN ML model here ✔