IN R programming
(a) Pre-processing: Check the data to see if there is any missing value. The simplest approach to deal with the missing values is to remove the rows with any missing values. Use are free to pick other methods from literature to deal with missing data. Remember that the final data provided to the regression models should not have any missing value. Also, make sure that the variables have the correct type. For example, “station” number doesn’t really have a numerical meaning! Or the date should not be included as a predictor.
(b) Split the data into three parts. Based on the “date”, consider the first 60% for training (Train), the next 20% for validation (Valid), and the last 20% for testing (Test). We will not touch the test set until the very end step.
(c) Initial Model: Fit a multiple linear regression model on the training data. Use this model to make prediction on the validation set. Then, calculate RMSE on the validation set using the following formula in below pic
Explore and discuss this model, its quality, significance of variables, residuals,
Bu iş için 15 freelancer ortalamada $17/saat teklif veriyor
Hi, I have a lot of experience with the R language. I also have a master's degree in data science. My reviews prove to you that I worked well on R projects. Your project is a challenge for me. Let's discuss it.
I am writing to propose a project for the pre-processing, splitting, and initial modeling of R programming data. The project covers the following objectives: 1. Pre-processing: Check the data to see if there is any mi Daha Fazla
In r programming fit multiple linear regression model Dear client, I have gone through your project and understand that I am a determined person concerned with providing quality work for a cheaper cost to my client; I Daha Fazla
Hi, I have understood that you need Machine Learning Expert using R for Linear Regression model. I offer my services for this project. I have made many Machine Learning based projects using R and Python as follows; • H Daha Fazla