Skip to content
Contact →

05-Linear-Regression-Execise

Updated
17 Sept 2026
1 min read
0 sections
Page 55 of 117
Python & Machine Learning
python
df = pd.read_csv("Advertising.csv")
python
df.head()
Out · table
TV
radio
newspaper
sales
0
24 more lines
230.1
37.8
69.2
22.1
1
44.5
39.3
45.1
10.4
2
17.2
45.9
69.3
9.3
3
151.5
41.3
58.5
18.5
4
180.8
10.8
58.4
12.9
python
fig,axes = plt.subplots(nrows=1, ncols=3, figsize=(16,6))

axes[0].plot(df['TV'], df['sales'], 'o')
axes[0].set_ylabel('Sales')
axes[0].set_xlabel('TV Advertising Budget')

axes[1].plot(df['radio'], df['sales'], 'o')
axes[1].set_ylabel('Sales')
axes[1].set_xlabel('Radio Advertising Budget')

axes[2].plot(df['newspaper'], df['sales'], 'o')
axes[2].set_ylabel('Sales')
axes[2].set_xlabel('Newspaper Advertising Budget')
Out · result
Text(0.5, 0, 'Newspaper Advertising Budget')
Out · plot
Output 1
python
sns.pairplot(df, diag_kind='kde')
Out · result
<seaborn.axisgrid.PairGrid at 0x121816960>
Out · plot
Output 2
python
X = df.drop('sales', axis=1)
y = df['sales']
python
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
python
X_train
Out · table
TV
radio
newspaper
79
116.0
43 more lines
7.7
23.1
197
177.0
9.3
6.4
38
43.1
26.7
35.1
24
62.3
12.6
18.3
122
224.0
2.4
15.6
...
...
...
...
106
25.0
11.0
29.7
14
204.1
32.9
46.0
92
217.7
33.5
59.0
179
165.6
10.0
17.6
102
280.2
10.1
21.4
160 rows × 3 columns
python
y_train
Out · result
79     11.0
197    12.8
38     10.1
24      9.7
122    11.6
7 more lines
       ... 
106     7.2
14     19.0
92     19.4
179    12.6
102    14.8
Name: sales, Length: 160, dtype: float64
python
from sklearn.linear_model import LinearRegression
python
model = LinearRegression()

# Model Training on Data
model.fit(X_train, y_train)
Out · table
LinearRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LinearRegression
?Documentation for LinearRegressioniFitted
Parameters
10 more lines
fit_intercept
True
copy_X
True
tol
1e-06
n_jobs
None
positive
False
python
test_predictions = model.predict(X_test)
python
test_predictions
Out · result
array([16.4080242 , 20.88988209, 21.55384318, 10.60850256, 22.11237326,
       13.10559172, 21.05719192,  7.46101034, 13.60634581, 15.15506967,
        9.04831992,  6.65328312, 14.34554487,  8.90349333,  9.68959028,
       12.16494386,  8.73628397, 16.26507258, 10.27759582, 18.83109103,
       19.56036653, 13.25103464, 12.33620695, 21.30695132,  7.82740305,
3 more lines
        5.80957448, 20.75753231, 11.98138077,  9.18349576,  8.5066991 ,
       12.46646769, 10.00337695, 21.3876709 , 12.24966368, 18.26661538,
       20.13766267, 14.05514005, 20.85411186, 11.0174441 ,  4.56899622])
python
from sklearn.metrics import mean_absolute_error,mean_squared_error
python
MAE = mean_absolute_error(y_test, test_predictions)
MSE = mean_squared_error(y_test, test_predictions)
RMSE = np.sqrt(MSE)
python
print(f"Mean Absolute Error: {MAE}")
print(f"Mean Squared Error: {MSE}")
print(f"Root Mean Squared Error: {RMSE}")
Out · stdout
Mean Absolute Error: 1.4607567168117606
Mean Squared Error: 3.174097353976105
Root Mean Squared Error: 1.7815996615334504
python
df['sales'].mean()
Out · result
np.float64(14.0225)
python
quartet = pd.read_csv('anscombes_quartet1.csv')
python
quartet['pred_y'] = 3 + 0.5 * quartet['x']
quartet['residual'] = quartet['y'] - quartet['pred_y']

sns.scatterplot(data=quartet, x='x', y='y')
sns.lineplot(data=quartet, x='x', y='pred_y', color='red')
plt.vlines(x=quartet['x'], ymin=quartet['pred_y'], ymax=quartet['y'], color='green', alpha=0.5)
Out · result
<matplotlib.collections.LineCollection at 0x1245f70b0>
Out · plot
Output 3
python
sns.kdeplot(quartet['residual'])
Out · result
<Axes: xlabel='residual', ylabel='Density'>
Out · plot
Output 4
python
sns.scatterplot(data=quartet,x='y',y='residual')
plt.axhline(y=0, color='r', linestyle='--')
Out · result
<matplotlib.lines.Line2D at 0x124730d40>
Out · plot
Output 5
python
quartet = pd.read_csv('anscombes_quartet2.csv')
python
quartet.columns = ['x','y']
python
# y = 3.00 + 0.500x
quartet['pred_y'] = 3 + 0.5 * quartet['x']
quartet['residual'] = quartet['y'] - quartet['pred_y']

sns.scatterplot(data=quartet,x='x',y='y')
sns.lineplot(data=quartet,x='x',y='pred_y',color='red')
plt.vlines(quartet['x'],quartet['y'],quartet['y']-quartet['residual'])
Out · result
<Axes: xlabel='x', ylabel='y'>
Out · plot
Output 6