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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() |