regression from machine learning

Question

Parameter	Description
Cement	Cement (kg in a m3 mixture)
BFSlag	Blast Furnace Slag – non-metallic co-product produced during a furnace operation(kg in a m^3 mixture)
FlyAsh	Fly Ash – Ash produced from burning coal (kg in a m3 mixture)
Water	Water (kg in a m3 mixture)
Superplasticizer	Superplasticizer – Additive for preventing aggregate formation (kg in a m^3 mixture)
CoarseAggregate	Coarse Aggregate - typically gravel (kg in a m3 mixture)
FineAggregate	Fine Aggregate – crushed stone or sand between 9.5 mm and 75 µm (kg in a m3 mixture)
Age	Age (day)
Ccstrength	Concrete compressive strength(MPa, megapascals)

1.    The total number of missing values within the dataset.

2.    Which column / feature requires correction in the type of value they hold?

3.    After imputation of nulls with mean what is the average value of the compressive strength in concrete?

4.    The feature that has a moderately strong relationship with compressive strength in concrete is?

5.Standardize the dataset using standardscaler(), split the dataset into train and test of proportions 70:30 and set the random state to 1. Build a Linear Regression Model on the data and the resulting r-squared value

Answer 1

I can't do your homework for you, not that that's even what you want, of course.. However, I have a lot experience with ML regression, so if you can ask a more specific question, I would be happy to help.