1. Conduct exploratory data analysis on the data set prior to building classification and
prediction models. In the report, do not devote a lot of space to discussions of dead-
ends, the pursuit of unproductive ideas, technology problems, etc.
2. Develop a classification model for the DONR variable using any of the variables as
predictors (except ID and DAMT).Fit at least four candidate models (e.g., KNN,
Neural Network, logistic regression, random forest; not 4 specificiations of the
same algorithm) training data and evaluate the fitted models using the validation
data. Use “maximum profit” as the evaluation criteria and use your final selected
classification model to classify DONR responses in the test dataset.
3. Develop a prediction model for the DAMT variable using any of the variables as
predictors (except ID and DONR). Fit at least four candidate models (least squares
regression, best subset selection, principal components regression, partial least
squares, regression trees, etc.) using the training data and evaluate the fitted
models using the validation data. Use “root mean squared error error” as the
evaluation criteria and use your final selected prediction model to predict DAMT
responses in the test dataset.
o To be clear, use the training data to fit your model, and the validation dataset to
validate your model. Once you have selected your final model, retrain your final
model using both the training and validation model, and use this final fitted model
to generate your predictions for the test dataset.
4. You must write up your course project results in a professional report.
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Hi, I have +5 experience dealing with machine learning algorithms and worked on multiple projects in this field, Please contact me to discuss more. Have a nice day