Delete app.py

app.py

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-# -*- coding: utf-8 -*-
-"""logistic_regression.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/1-Slk6y5-E3eUnmM4vjtoRrGMoIKvD0hU
-
-# Logistic Regression
-
-## Importing the libraries
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-import pandas as pd
-
-"""## Importing the dataset"""
-
-dataset = pd.read_csv('Social_Network_Ads.csv')
-X = dataset.iloc[:, :-1].values
-y = dataset.iloc[:, -1].values
-
-"""## Splitting the dataset into the 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)
-
-print(X_train)
-
-print(y_train)
-
-print(X_test)
-
-print(y_test)
-
-"""## Feature Scaling"""
-
-from sklearn.preprocessing import StandardScaler
-sc = StandardScaler()
-X_train = sc.fit_transform(X_train)
-X_test = sc.transform(X_test)
-
-print(X_train)
-
-print(X_test)
-
-"""## Training the Logistic Regression model on the Training set"""
-
-from sklearn.linear_model import LogisticRegression
-classifier = LogisticRegression(random_state = 0)
-classifier.fit(X_train, y_train)
-
-"""## Predicting a new result"""
-
-print(classifier.predict(sc.transform([[30,87000]])))
-
-"""## Predicting the Test set results"""
-
-y_pred = classifier.predict(X_test)
-print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))
-
-"""## Making the Confusion Matrix"""
-
-from sklearn.metrics import confusion_matrix, accuracy_score
-cm = confusion_matrix(y_test, y_pred)
-print(cm)
-accuracy_score(y_test, y_pred)
-
-"""## Visualising the Training set results"""
-
-from matplotlib.colors import ListedColormap
-X_set, y_set = sc.inverse_transform(X_train), y_train
-X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 10, stop = X_set[:, 0].max() + 10, step = 0.25),
-                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 0.25))
-plt.contourf(X1, X2, classifier.predict(sc.transform(np.array([X1.ravel(), X2.ravel()]).T)).reshape(X1.shape),
-             alpha = 0.75, cmap = ListedColormap(('salmon', 'dodgerblue')))
-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(('salmon', 'dodgerblue'))(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 = sc.inverse_transform(X_test), y_test
-X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 10, stop = X_set[:, 0].max() + 10, step = 0.25),
-                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 0.25))
-plt.contourf(X1, X2, classifier.predict(sc.transform(np.array([X1.ravel(), X2.ravel()]).T)).reshape(X1.shape),
-             alpha = 0.75, cmap = ListedColormap(('salmon', 'dodgerblue')))
-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(('salmon', 'dodgerblue'))(i), label = j)
-plt.title('Logistic Regression (Test set)')
-plt.xlabel('Age')
-plt.ylabel('Estimated Salary')
-plt.legend()
-plt.show()