This is a detailed explanation of the understanding of linear regression with Boston Housing Dataset. I have performed detailed checks to know more about the relationship between the variables used in the dataset. Plotted visualizations which gives different intuitions about collinearity and also came up with plotting the best fit line of all important features with the target variable.

The Boston Housing Dataset is a derived from information collected by the U.S. Census Service concerning housing in the area of Boston MA. The following describes the dataset columns:

• CRIM - per capita crime rate by town
• ZN - proportion of residential land zoned for lots over 25,000 sq.ft.
• INDUS - proportion of non-retail business acres per town.
• CHAS - Charles River dummy variable (1 if tract bounds river; 0 otherwise)
• NOX - nitric oxides concentration (parts per 10 million)
• RM - average number of rooms per dwelling
• AGE - proportion of owner-occupied units built prior to 1940
• DIS - weighted distances to five Boston employment centres
• RAD - index of accessibility to radial highways
• TAX - full-value property-tax rate per $10,000
• PTRATIO - pupil-teacher ratio by town
• B - 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
• LSTAT - % lower status of the population
• MEDV - Median value of owner-occupied homes in $1000's

MEDV is the dependent variable.