In this assignment, we will practice loading datasets from Pandas, visualization, and writing processing pipelines. We will work with the auto$-$mpg data in which each row carries information for a car model. The data has the following columns:

$1.$ mpg:$$ mile per gallon rate of the car

$2.$ cylinders:$$ number of cylinders the car has

$3.$ displacement: $$ displacement of the car

$4.$ horsepower:$$ horsepower of the car

$5.$ weight:$$ weight of the car

$6.$ acceleration:$$ acceleration of the car

$7.$ model year:$$ the year the car was introduced

$8.$ origin:$$ the development location of the car $($number codes represent Asia, Europe, and North America$)$

$9.$ car name:$$ model name of the car $($unique for each car model$)$

Please use the auto$-$mpg$.$csv data file to do the following:

$1.$ Load the data into a Python session as a Pandas DataFrame. Check if all columns are in correct type, and fix any incorrect ones if necessary

$2.$ Split the data $75\%$ training and $25\%$ testing

$3.$ Visualize necessary columns in the data. After this point, you should have three lists:

$$ Columns that are numeric and have symmetric distributions

$$ Columns that are numeric and have skewed distributions

$$ Columns that are categorical

$4.$ Build a pipeline as follows:

$$ Numeric and symmetric columns: $$ Imputation $$ Standardization

$$ Numeric and skewed columns:$$ Imputation $$ Log transformation $$ Standardization

$$ Categorical columns: $$ One hot encoder

$5.$ Train the pipeline on the training data. Then perform transformation on the training data and testing data.

$6.$ Print the shape of the processed training data and processed testing data.