Machine Learning with the mtcars Dataset

Objectives:

Perform data preprocessing on the mtcars dataset.

Build and evaluate multiple regression models to predict a suitable target variable.

Discuss challenges and considerations when using a small dataset.

Instructions:

Load the mtcars dataset:

This is a built in dataset

Explore the data:

Summarize key statistics, identify missing values $($if any$),$ and visualize distributions of variables.

Choose a suitable target variable for regression $($e$.$g$.,$ mpg$,$ horsepower, weight$).$ You can try to use varaible other than MPG

Preprocess the data:

Handle missing values $($if present$)$ using appropriate techniques like imputation or removal.

Consider outlier treatment if necessary.

Create binary features from categorical variables $($if applicable$).$

Split the data:

Split the data into training and testing sets using an appropriate ratio $($e$.$g$.,70/30,80/20).$

Consider using stratified sampling if your target variable is categorical.

Scale the features:

Scale the features to ensure each variable has equal importance in the models.

Build and evaluate models:

Build and evaluate at all five different regression models we learned in class $($e$.$g$.,$ linear regression, Polynomial Regression, SVR$,$ Decision Tree

and Random forest$).$

Consider hyperparameter tuning for models that benefit from it $($e$.$g$.,$ SVR$,$ random forest$).$

Use appropriate metrics for evaluation $($e$.$g$.,$ mean squared error, R$-$squared, adjusted R$-$squared$).$

Create visualizations to compare model performance $($e$.$g$.,$ scatter plots, residual plots$).$

Discuss challenges and considerations:

Discuss the limitations of using a small dataset like mtcars for regression.

Explain potential challenges you encountered $($e$.$g$.,$ overfitting, limited feature selection$).$

Suggest potential techniques to mitigate these challenges $($e$.$g$.,$ cross$-$validation, regularization$).$

Conclusion:

Summarize your findings and recommend the best model$($s$)$ for predicting the chosen target variable in mtcars.

Discuss the generalizability of your results and potential further research directions.

Write a report on your findings, including your recommendations on which model$($s$)$ to use for predicting your chosen dependent varaible.

Deliverables:

R Code as R or RMD file $(10$ points$)$

Written report as MS Word or PDF $(20$ points$)-$ Yes the report MUST be separate.