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Connecting to data sources using IDE is one of the first steps in big data handl
Connecting to data sources using IDE is one of the first steps in big data handling. This week, you will be using Jupyter Notebook to import and process the Boston dataset (dataset is provided). The attributes of the data are:
Attribute Information (in order):
– 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 before 1940
– DIS weighted distances to five Boston employment centers
– 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
Complete the following steps:
Import housing data into your Jupyter Notebook
Include the shape of the data using Python’s shape method
Include the info of the data using the Python’s info method
Include the summary statistics of the data using the described method.
Include the screen print of your work with short descriptions of each method used to import and describe the dataset in your paper.
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