Predict Values based on readings Best approach
I was able to figure it out I just ran a loop to train on each dependant var separately. if you have a big dataset like 300,000 using random forest take about 2.5- 3 seconds per dependant var and then used the missing data as a df to find predictions and append. if you need more explanation let me know
I am working on a project where I am given a round 200 readings/featured columns and based on those reading there are some attributes about 60(columns) of them ranked from 0-5. now I have about 1000 rows from the featured readings and only 100 from the attributes. I am looking to use a model that I can train the data with the 100 attributes filled out and then predict on the remaining 900 attributes rows from the featured data given.
Are there are any recommendations for the best approach or even better a similar project I can reference?