The code attempts foloowing tasks,

• Read the numerical housing dataset with 14 columns and 506 rows from file, 'housing-data.txt'
• Visualize the correlation between output value and other attributes.
• Define, train and test a machine learning model to predict the output when inputs are provided.

The dataset has first 13 columns as input attributes and the 14th column as output value.

Information of the dataset

• Column 0, CRIM, per capita crime rate by town
• Column 1, ZN, proportion of residential land zoned for lots over 25,000 sq.ft.
• Column 2, INDUS, proportion of non-retail business acres per town
• Column 3, CHAS, Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
• Column 4, NOX, nitric oxides concentration (parts per 10 million)
• Column 5, RM, average number of rooms per dwelling
• Column 6, AGE, proportion of owner-occupied units built prior to 1940
• Column 7, DIS, weighted distances to five Boston employment centres
• Column 8, RAD, index of accessibility to radial highways
• Column 9, TAX, full-value property-tax rate per 10000 US Dollar
• Column 10, PTRATIO, pupil-teacher ratio by town
• Column 11, B, 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
• Column 12, LSTAT, % lower status of the population
• Column 13, MEDV, Median value of owner-occupied homes in $1000's