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Machine Learning Practice · End Term · 24 Dec 2023 · September 2023 term · Set FDD1

Question 37: Please consider the following data and code for a regres…

Question 37

+3 marksNumerical answer

Please consider the following data and code for a regression problem with following symbols in mind:

  • >>>: Represents input code
  • # : Represents comment in a code
  • ... : Represents code continuation
  • Without any symbols at the beginning of a line then it is output of just above input line of code.
AgeCar_colorAccidents_per_1000_Driver
019Black74
119Blue75
219Red85
324Black70
424Blue70
524Red89
630Black78
730Blue76
830Red90

Target column: Accidents_per_1000_Driver

python
>>> import pandas as pd
>>> from sklearn.preprocessing import OneHotEncoder
>>> from sklearn.linear_model import LinearRegression


>>> data = pd.DataFrame([[19, 'Black', 74],
...                      [19, 'Blue', 75],
...                      [19, 'Red', 85],
...                      [24, 'Black', 70],
...                      [24, 'Blue', 70],
...                      [24, 'Red', 89],
...                      [30, 'Black', 78],
...                      [30, 'Blue', 76],
...                      [30, 'Red', 90]],
                        columns=["Age","Car_color","Accidents_per_1000_Driver"])


>>> X = data.drop("Accidents_per_1000_Driver", axis=1)
>>> y = data["Accidents_per_1000_Driver"]


>>> ohe = OneHotEncoder(sparse_output=False)


>>> X[['Black', 'Blue', 'Red']] = ohe.fit_transform(X[["Car_color"]])
>>> X.drop("Car_color", axis=1, inplace=True)


>>> lr = LinearRegression().fit(X, y)


>>> print(lr.coef_)
[0.32, -4.55, -4.88, 9.44]


>>> print(lr.intercept_)
70.75

Based on the above data, answer the given subquestions.

How many Accidents per 1000 Driver happens for Age 25 and driving blue car ?

Show answer

Correct answer: 74 (accepted within ±1)

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Question 37 of 38 in the IIT Madras BS Machine Learning Practice (MLP) End Term paper sat on 24 Dec 2023, in the September 2023 term (IIT M DIPLOMA FN EXAM FDD1 24 Dec 2023). It carries 3 marks.

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