Logistic.py

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
X_train, X_test, y_train, y_test = 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, :])
Output: 

[[ 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.