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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
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 eg 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 eg
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 eg linear regression, Polynomial Regression, SVR Decision Tree
and Random forest
Consider hyperparameter tuning for models that benefit from it eg SVR random forest
Use appropriate metrics for evaluation eg mean squared error, Rsquared, adjusted Rsquared
Create visualizations to compare model performance eg 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 eg overfitting, limited feature selection
Suggest potential techniques to mitigate these challenges eg crossvalidation, regularization
Conclusion:
Summarize your findings and recommend the best models 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 models to use for predicting your chosen dependent varaible.
Deliverables:
R Code as R or RMD file points
Written report as MS Word or PDF points Yes the report MUST be separate.
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