Pythonic Way for Logistic Regression Tutorial Machine Learning Classification Example¶

sklearn.linear_model.LogisticRegression¶

This Example is Complete guide for Logistic Regression¶

Soumil Nitin Shah¶

Bachelor in Electronic Engineering | Masters in Electrical Engineering | Master in Computer Engineering |

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Hello! I’m Soumil Nitin Shah, a Software and Hardware Developer based in New York City. I have completed by Bachelor in Electronic Engineering and my Double master’s in Computer and Electrical Engineering. I Develop Python Based Cross Platform Desktop Application , Webpages , Software, REST API, Database and much more I have more than 2 Years of Experience in Python

In [31]:

import pandas as pd
df = pd.read_csv('advertising.csv')
df.head(2)

Out[31]:

	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

We want to learn tp classify based on Dataset whether the person clicked on the AD or Not

In [32]:

import pandas as pd
import seaborn as sns

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix

import scikitplot as skplt

class NN():
    
    def __init__(self, max_iter=150):
        self.max_iter=max_iter
        self.X_Train, self.X_Test, self.Y_Train, self.Y_Test = self.preprocess
        self.model = self.create_model
    
    @property
    def preprocess(self):
        df = pd.read_csv('advertising.csv')
        X_Data = df[['Daily Time Spent on Site', 'Age', 'Area Income','Daily Internet Usage', 'Male']]
        Y_Data = df ['Clicked on Ad']
        X_Train, X_Test, Y_Train, Y_Test = train_test_split(X_Data, Y_Data, test_size=0.4, random_state=101)
        return X_Train, X_Test, Y_Train, Y_Test 
    
    @property
    def create_model(self):
        """
        return : Model Object 
        """
        model = LogisticRegression(max_iter=self.max_iter)
        return model
        
    
    @property
    def train(self):
        """
        return None Train the Model
        """
        self.model.fit(self.X_Train, self.Y_Train)
        
    @property   
    def test(self):
        """
        return pred [Array ]
        return coef_ [array]
        return intercept_ [array]
        """
        pred = self.model.predict(self.X_Test)
        return pred,self.model.coef_ , self.model.intercept_
    
    @property
    def download_report(self):
        """
        return confusion matrix 
        return classification report
        return plots the confusion matrix 
        """
        pred, coef_ , intercept_ =self.test
        report = classification_report(self.Y_Test, pred)
        matrix = confusion_matrix(self.Y_Test, pred)
        skplt.metrics.plot_confusion_matrix(self.Y_Test, 
                                            pred,
                                           figsize=(6,6),
                                           title="Confusion Matrix")
        return report, matrix
    
    @property
    def plot(self):
        pass

In [33]:

neural = NN()
neural.train
pred,coef_,intercept_ = neural.test
report, matrix = neural.download_report
print(result)

              precision    recall  f1-score   support

           0       0.89      0.93      0.91       206
           1       0.92      0.88      0.90       194

   micro avg       0.91      0.91      0.91       400
   macro avg       0.91      0.90      0.90       400
weighted avg       0.91      0.91      0.90       400

/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
  FutureWarning)