[R] Help needed! Pre-processing the dataset before splitting - model building - model tuning - performance evaluation

[Rui Barradas]

Às 06:04 de 24/09/2024, Bekzod Akhmuratov escreveu:
> Below is the link for a dataset on focus. I want to split the dataset into
> training and test set, use training set to build the model and model tune,
> use test set to evaluate performance. But before doing that I want to make
> sure that original dataset doesn't have noise, collinearity to address, no
> major outliers so that I have to transform the data using techniques like
> Box-Cox and looking at VIF to eliminate highly correlated predictors.
> 
> https://www.kaggle.com/datasets/joaofilipemarques/google-advanced-data-analytics-waze-user-data
> 
> When I fit the original dataset into regression model with Minitab, I get
> attached result for residuals. It doesn't look normal. Does it mean there
> is high correlation or the dataset in have nonlinear response and
> predictors? How should I approach this? What would be my strategy if I use
> in Python, Minitab, and R. Explaining it in all softwares are appraciated
> if possible.
> 
> 
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Hello,

R-Help is a list of questions and answers about R code, not to suggest 
analysis strategies. Anyhow, I suggest that you read the Python notebook 
at the bottom of the Kaggle page, it addresses your main question and if 
you have doubts translating the Python code to R code, ask us more 
specific questions on those doubts.

Hope this helps,

Rui Barradas


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