Dataframe/Python URL="https://bit.ly/2WKPUXI"

*You may need to use encoding='latin1' as additional parameter for read_csv()

Q1:

from sklearn.linear_model import LinearRegression

from sklearn.metrics import mean_absolute_error

model = LinearRegression()

model.fit(X_train, y_train)

predict = model.predict(X_test)

r_sq = model.score(X_test, y_test)

print('coefficient of determination:', r_sq)

print("errors in predictions: ", mean_absolute_error(y_test, predict)) # summation| predict - real|

print("coefficient: ", model.coef_).

Justify the reasons behind the error you got.

Q2: Discovering the Data.

Q3: Handle the missing values as follows:

• Drop the columns that include 95% missing data.
• Estimate missing values if only an acceptable percentage of values are missing. Hint, you are supposed to depend on the relations between columns (e.g., ("car" and "engV"), ("model" and "drive")).
• Drop the rows that still contain missing values

Q4: Check outliers and suggest a solution if it is necessary.