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logistic_regression Template.py
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# Logistic Regression
# Importing the Libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# Importing the dataset
dataset = pd.read_csv('Social_Network_Ads.csv')
# Defining Independent and Dependent Variables
X = dataset.iloc[:, [2,3]].values
y = dataset.iloc[:, 4].values
# Which Regression?
plt.scatter(X[:,0], y, color = 'red')
plt.scatter(X[:,1], y, color = 'red')
# Splitting the dataset into training set and test set
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)
# Feature Scaling
from sklearn.preprocessing import StandardScaler
SC_X = StandardScaler()
X_train = SC_X.fit_transform(X_train)
X_test = SC_X.transform(X_test)
# Fitting Logistic Regression to the Training set
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state= 0)
classifier.fit(X_train, y_train)
# Prediciting the Test set results and X_new
y_pred = classifier.predict(X_test)
X_new = SC_X.fit_transform(np.array([[19, 19000]], dtype = float)) # it should be normalized
classifier.predict(X_new)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
# accuracy = (TP+TN)/(TP+FP+TN+FN)
# recall = TP/(TP+FN)
# precision = TP/(TP+FP)
from sklearn.metrics import precision_recall_fscore_support
precision_recall_fscore_support(y_test, y_pred)
# Visualising the Training set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_train, y_train
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 = 0.5, camp = 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('Logistic Regression (Training set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
# Visualising the Test set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_test, y_test
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 = 0.5, camp = 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('Logistic Regression (Test set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()