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Logistic Regression using Python

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User Database – This dataset contains information about users from a company’s database. It contains information about UserID, Gender, Age, EstimatedSalary, and Purchased. We are using this dataset for predicting whether a user will purchase the company’s newly launched product or not.

Prerequisite: Understanding Logistic Regression

Do refer to the below table from where data is being fetched from the dataset.

Let us make the Logistic Regression model, predicting whether a user will purchase the product or not. 
Inputting Libraries.

Import Libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

Read and Explore the data


dataset = pd.read_csv("User_Data.csv")

Now, to predict whether a user will purchase the product or not, one needs to find out the relationship between Age and Estimated Salary. Here User ID and Gender are not important factors for finding out this. 


# input
x = dataset.iloc[:, [2, 3]].values
# output
y = dataset.iloc[:, 4].values

Splitting The Dataset: Train and Test dataset

Splitting the dataset to train and test. 75% of data is used for training the model and 25% of it is used to test the performance of our model.  


from sklearn.model_selection import train_test_split
xtrain, xtest, ytrain, ytest = train_test_split(
    x, y, test_size=0.25, random_state=0)

Now, it is very important to perform feature scaling here because Age and Estimated Salary values lie in different ranges. If we don’t scale the features then the Estimated Salary feature will dominate the Age feature when the model finds the nearest neighbor to a data point in the data space. 


from sklearn.preprocessing import StandardScaler
sc_x = StandardScaler()
xtrain = sc_x.fit_transform(xtrain)
xtest = sc_x.transform(xtest)
print (xtrain[0:10, :])


[[ 0.58164944 -0.88670699]
 [-0.60673761  1.46173768]
 [-0.01254409 -0.5677824 ]
 [-0.60673761  1.89663484]
 [ 1.37390747 -1.40858358]
 [ 1.47293972  0.99784738]
 [ 0.08648817 -0.79972756]
 [-0.01254409 -0.24885782]
 [-0.21060859 -0.5677824 ]
 [-0.21060859 -0.19087153]]

Here once see that Age and Estimated salary features values are scaled and now there in the -1 to 1. Hence, each feature will contribute equally to decision making i.e. finalizing the hypothesis. 

Finally, we are training our Logistic Regression model. 

Train The Model


from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state = 0), ytrain)

After training the model, it is time to use it to do predictions on testing data. 


y_pred = classifier.predict(xtest)

Let’s test the performance of our model – Confusion Matrix 

Evaluation Metrics

Metrics are used to check the model performance on predicted values and actual values. 


from sklearn.metrics import confusion_matrix
cm = confusion_matrix(ytest, y_pred)
print ("Confusion Matrix : \n", cm)


Confusion Matrix : 
 [[65  3]
 [ 8 24]]

Out of 100 : 
True Positive + True Negative = 65 + 24 
False Positive + False Negative = 3 + 8
Performance measure – Accuracy 



from sklearn.metrics import accuracy_score
print ("Accuracy : ", accuracy_score(ytest, y_pred))


Accuracy :  0.89

Visualizing the performance of our model. 


from matplotlib.colors import ListedColormap
X_set, y_set = xtest, ytest
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1
                               stop = X_set[:, 0].max() + 1, step = 0.01),
                     np.arange(start = X_set[:, 1].min() - 1
                               stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(
             np.array([X1.ravel(), X2.ravel()]).T).reshape(
             X1.shape), alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
                c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('Classifier (Test set)')
plt.ylabel('Estimated Salary')



Analyzing the performance measures – accuracy and confusion matrix and the graph, we can clearly say that our model is performing really well.

Last Updated : 20 Mar, 2023
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