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.

Report:

Avoid putting any code into your report. You are submitting your code separately, so there is no need to put in the written report. The same goes for PowerPoint presentations. Assume your audience have no idea how to code. Presenting code will only confuse your audience and diminish your presentation.

$1.$ 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