1) First, you need to do variable regression i.e for each column in your data set you do simple linear regression and calculate p-value... Thereby you get an idea of the significance of each column against the target variable.

2) plot influence plot check the cooks_d value

 import statsmodels.api as sm
  infl = model1.get_influence()
  sm_fr = infl.summary_frame()

3. You will get cooks_d value from sm_fr data frame

4)select the row point with a cooks_d value>1 and remove that row from your data frame,, now you have removed influential points. 5)Now check VIF values for new set data frame containing variables and remove the variables having vif>5 as they are insignificant ... you can also check their significance calcualting p value .

for overall procedure of building a multi linear regression model satisfying all assumotions of multilinear regression like linearity,homosedasticity,multivariate normality and no multicollineaity see the below example of prediction of profit of start- ups

https://github.com/tharun435/Data-Science-/blob/master/startups.ipynb

1) First, you need to do variable regression i.e for each column in your data set you do simple linear regression and calculate p-value... Thereby you get an idea of the significance of each column against the target variable.

2) plot influence plot check the cooks_d value

 import statsmodels.api as sm
  infl = model1.get_influence()
  sm_fr = infl.summary_frame()

3. You will get cooks_d value from sm_fr data frame

4)select the row point with a cooks_d value>1 and remove that row from your data frame,, now you have removed influential points. 5)Now check VIF values for new set data frame containing variables and remove the variables having vif>5 as they are insignificant ... you can also check their significance calcualting p value .

for overall procedure of building a multi linear regression model satisfying all assumotions of multilinear regression like linearity,homosedasticity,multivariate normality and no multicollineaity see the below example of prediction of profit of start- ups

https://github.com/findtharun/Data-Science-/blob/master/startups.ipynb