Multiple Linear Regression

Question 1:
The code used to import the pandas library with its standard alias 'pd'

import pandas as pd

Question 2:
The code used to import the numpy library with its standard alias 'np'

import numpy as np

Question 3:
The code used to import matplotlib's pyplot module with its standard alias 'plt'

import matplotlib.pyplot as plt

Question 4:
The import statement for the traintestsplit function from scikit-learn

from sklearn.modelselection import traintest_split

Question 5:
The import statement for the LinearRegression class from scikit-learn

from sklearn.linear_model import LinearRegression

Question 6:
The import statement for both r2score and meansquared_error metrics from scikit-learn

from sklearn.metrics import r2score, meansquared_error

Question 7:
The function used to read a CSV file into a pandas DataFrame

pd.read_csv("filename.csv")

Question 8:
The method used to display the first few rows of a pandas DataFrame

df.head()

Question 9:
The method used to drop specific columns from a pandas DataFrame

df.drop(columns=['col1', 'col2'])

Question 10:
The method used to calculate the correlation matrix of a pandas DataFrame

df.corr()

Question 11:
The syntax for selecting multiple columns from a DataFrame to create a feature matrix X

X = df[['col1', 'col2']]

Question 12:
The syntax for selecting a single column from a DataFrame to create a target variable y

y = df['target_column']

Question 13:
The complete function call to split data into training and testing sets with 20% test size and random_state=42

Xtrain, Xtest, ytrain, ytest = traintestsplit(X, y, testsize=0.2, randomstate=42)

Question 14:
The code to create and instantiate a LinearRegression model

model = LinearRegression()

Question 15:
The method used to train/fit the LinearRegression model on data

model.fit(X, y)

Question 16:
The attribute that stores the regression coefficients (slopes) after fitting

model.coef_

Question 17:
The attribute that stores the y-intercept value after fitting

model.intercept_

Question 18:
The method used to make predictions with a trained LinearRegression model

model.predict(X_test)

Question 19:
The function to calculate the R² score given true values and predicted values

r2score(ytest, y_pred)

Question 20:
The function to calculate the Mean Squared Error given true values and predicted values

meansquarederror(ytest, ypred)

Question 21:
The code to predict CO2 emission for a single car with volume 3000 and weight 1500

model.predict([[3000, 1500]])

Question 22:
The complete code to load the CO2_CarEmission.csv file

df = pd.readcsv("CO2CarEmission.csv")

Question 23:
The complete code to drop the 'Car' and 'Model' columns from df

df = df.drop(columns=['Car', 'Model'])

Question 24:
The complete code to assign Volume and Weight as features X, and CO2 as target y

X = df[['Volume', 'Weight']]
y = df['CO2']

Question 25:
The complete code to train the model on the entire dataset and then predict on the test set

model = LinearRegression()
model.fit(X, y)
ypred = model.predict(Xtest)