A corporation wishes to forecast home selling prices using existing historical sales data. The selling price is the goal variable in the company's dataset. The attributes include the lot size, measures of the living space and non-living area, the number of bedrooms and bathrooms, the year constructed, and the postal code. The organisation wishes to forecast home selling prices using multivariable linear regression. Which step should a machine learning professional take to eliminate extraneous information and simplify the model?

(i). Plot a histogram of the features and compute their standard deviation.

(ii). Plot a histogram of the features and compute their standard deviation.

Remove features with high variance.

Remove features with low variance.

(iii). Build a heatmap showing the correlation of the dataset against itself. Remove features with low mutual correlation scores. (iv). Run a correlation check of all features against the target variable. Remove features with low target variable correlation scores.