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 variable.

Deliverables:

R Code as R or RMD file

Also a Report explaining the following:

$1.$ Clear Introduction:

State the Problem: Begin with a clear statement of the problem you are addressing in your analysis.

Objective: Clearly state the objective or what you intend to achieve through your analysis.

Scope: Define the scope of your analysis. What is included and what is not?

$2.$ Data Preprocessing:

Data Cleaning: Describe how you handled missing data, outliers, or errors in your dataset. Be clear about your approach.

Data Transformation: Explain any transformations performed on the data, such as normalization or encoding categorical variables.

Feature Engineering: If you created new features, explain why and how they are useful.

$3.$ Model Building:

Model Selection: Justify your choice of modeling techniques. Explain why you chose a particular algorithm.

Hyperparameter Tuning: Describe any hyperparameter tuning and why it was necessary.

Training and Testing: Explain how you divided the data into training and testing sets.

$4.$ Model Evaluation:

Performance Metrics: Clearly define and interpret the performance metrics you used $($e$.$g$.,$ accuracy, precision, recall, F$1-$score$).$

Visualizations: Use visualizations, like ROC curves, to illustrate model performance.

Comparison: Compare the results of different models or approaches, if applicable.

$5.$ Results Interpretation:

Explain Findings: Interpret the results. What do the numbers mean in the context of the problem?

Provide Insights: Share insights that you gained from your analysis.

$6.$ Recommendations:

Recommendations: Offer practical recommendations based on your findings. What should stakeholders do in response to your analysis?

$7.$ Clear Conclusion:

Summary: Summarize the key findings of your analysis.

Conclusion: Provide a clear and concise conclusion, reiterating the problem, the findings, and their significance.

Future Work: Suggest areas for further exploration or improvements to your analysis.

$8.$ Clarity and Organization:

Structure: Organize your report logically$,$ with clear headings and subheadings.

Use of Code: While it's okay to include code snippets, keep them separate in an appendix. Do not clutter the main report with code.

Visual Aids: Use figures, tables, and visual aids to make complex concepts more understandable.

$9.$ Language and Style:

Concise Language: Keep your writing clear, concise, and to the point.

Avoid Jargon: Define any technical terms or jargon you use.

Proofreading: Proofread your report for grammar and spelling errors.