Logistic Regression Project¶

K.D.P Kulugammana¶
In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
In [2]:
ad_data = pd.read_csv('advertising.csv')
In [3]:
ad_data.head()
Out[3]:
Daily Time Spent on Site Age Area Income Daily Internet Usage Ad Topic Line City Male Country Timestamp Clicked on Ad
0 68.95 35 61833.90 256.09 Cloned 5thgeneration orchestration Wrightburgh 0 Tunisia 2016-03-27 00:53:11 0
1 80.23 31 68441.85 193.77 Monitored national standardization West Jodi 1 Nauru 2016-04-04 01:39:02 0
2 69.47 26 59785.94 236.50 Organic bottom-line service-desk Davidton 0 San Marino 2016-03-13 20:35:42 0
3 74.15 29 54806.18 245.89 Triple-buffered reciprocal time-frame West Terrifurt 1 Italy 2016-01-10 02:31:19 0
4 68.37 35 73889.99 225.58 Robust logistical utilization South Manuel 0 Iceland 2016-06-03 03:36:18 0
In [4]:
ad_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 10 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Daily Time Spent on Site  1000 non-null   float64
 1   Age                       1000 non-null   int64  
 2   Area Income               1000 non-null   float64
 3   Daily Internet Usage      1000 non-null   float64
 4   Ad Topic Line             1000 non-null   object 
 5   City                      1000 non-null   object 
 6   Male                      1000 non-null   int64  
 7   Country                   1000 non-null   object 
 8   Timestamp                 1000 non-null   object 
 9   Clicked on Ad             1000 non-null   int64  
dtypes: float64(3), int64(3), object(4)
memory usage: 78.3+ KB
In [5]:
ad_data.describe()
Out[5]:
Daily Time Spent on Site Age Area Income Daily Internet Usage Male Clicked on Ad
count 1000.000000 1000.000000 1000.000000 1000.000000 1000.000000 1000.00000
mean 65.000200 36.009000 55000.000080 180.000100 0.481000 0.50000
std 15.853615 8.785562 13414.634022 43.902339 0.499889 0.50025
min 32.600000 19.000000 13996.500000 104.780000 0.000000 0.00000
25% 51.360000 29.000000 47031.802500 138.830000 0.000000 0.00000
50% 68.215000 35.000000 57012.300000 183.130000 0.000000 0.50000
75% 78.547500 42.000000 65470.635000 218.792500 1.000000 1.00000
max 91.430000 61.000000 79484.800000 269.960000 1.000000 1.00000

Exploratory Data Analysis¶

In [6]:
sns.set_style('whitegrid')
ad_data['Age'].hist(bins=30)
plt.xlabel('Age')
Out[6]:
Text(0.5, 0, 'Age')
In [7]:
sns.jointplot(x='Age',y='Area Income',data=ad_data)

C:\Users\ACER\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\ACER\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
Out[7]:
<seaborn.axisgrid.JointGrid at 0x1e3bf54a450>
In [8]:
sns.jointplot(x='Age',y='Daily Time Spent on Site',data=ad_data,color='red',kind='kde');

C:\Users\ACER\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\ACER\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\ACER\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\ACER\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
In [9]:
sns.jointplot(x='Daily Time Spent on Site',y='Daily Internet Usage',data=ad_data,color='green')

C:\Users\ACER\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\ACER\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
Out[9]:
<seaborn.axisgrid.JointGrid at 0x1e3c0375dd0>
In [10]:
sns.pairplot(ad_data,hue='Clicked on Ad',palette='bwr')

C:\Users\ACER\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\ACER\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\ACER\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\ACER\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\ACER\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\ACER\anaconda3\Lib\site-packages\seaborn\axisgrid.py:118: UserWarning: The figure layout has changed to tight
  self._figure.tight_layout(*args, **kwargs)
Out[10]:
<seaborn.axisgrid.PairGrid at 0x1e3c186f8d0>

Logistic Regression¶

In [11]:
from sklearn.model_selection import train_test_split
In [12]:
X = ad_data[['Daily Time Spent on Site', 'Age', 'Area Income','Daily Internet Usage', 'Male']]
y = ad_data['Clicked on Ad']
In [13]:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

Train and fit a logistic regression model on the training set.

In [14]:
from sklearn.linear_model import LogisticRegression
In [15]:
logmodel = LogisticRegression()
logmodel.fit(X_train,y_train)
Out[15]:
LogisticRegression()
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LogisticRegression()

Predictions and Evaluations¶

In [16]:
predictions = logmodel.predict(X_test)

Create a classification report for the model.

In [17]:
from sklearn.metrics import classification_report
In [18]:
print(classification_report(y_test,predictions))

              precision    recall  f1-score   support

           0       0.86      0.96      0.91       162
           1       0.96      0.85      0.90       168

    accuracy                           0.91       330
   macro avg       0.91      0.91      0.91       330
weighted avg       0.91      0.91      0.91       330
In [21]:
coefficients = logmodel.coef_[0]
feature_names = X.columns

# Create a coefficient table
coeff_table = pd.DataFrame({'Feature': feature_names, 'Coefficient': coefficients})

# Display the coefficient table
print(coeff_table)

                    Feature  Coefficient
0  Daily Time Spent on Site    -0.054180
1                       Age     0.260352
2               Area Income    -0.000017
3      Daily Internet Usage    -0.026712
4                      Male     0.002311
In [22]:
from sklearn import metrics

print('MAE:', metrics.mean_absolute_error(y_test, predictions))
print('MSE:', metrics.mean_squared_error(y_test, predictions))
print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))

MAE: 0.09393939393939393
MSE: 0.09393939393939393
RMSE: 0.3064953408118856