# Importing the libraries import statsmodels.api as sm from sklearn.linear_model import LinearRegression import sys import os import numpy as np if '__file__' not in globals(): sys.path.append( os.getcwd() + 'Machine_Learning_A-Z_Mine/Part 2 - Regression/Section 5 - Multiple Linear Regression' ) import data_preprocessing_template as preprocessed_data # %% codecell # preprocess data x_train, x_test, y_train, y_test, x, y = preprocessed_data.preprocess_data() # %% codecell # Fitting Simple Linear Regression to the Training set regressor = LinearRegression() regressor.fit(x_train, y_train) # %% codecell # Predicting the Test set results y_pred = regressor.predict(x_test) # %% codecell # backwords elimination # add constant to function x = np.append(np.ones((50, 1), dtype=np.int), x, axis=1)
# Importing the libraries import sys import os from sklearn.svm import SVC from sklearn.metrics import confusion_matrix import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap import numpy as np if '__file__' not in globals(): sys.path.append(os.getcwd() + '/Machine_Learning_A-Z_Mine/Part 3 - Classification/Section 16 - Support Vector Machine (SVM)') import data_preprocessing_template as preprocessed_data # %% codecell # preprocess data x_train, x_test, y_train, y_test, sc_x = preprocessed_data.preprocess_data() # %% codecell # Fitting SVM to the Training set classifier = SVC(kernel="linear", random_state= 0) classifier.fit(x_train, y_train) # %% codecell # Predicting the Test set results y_pred = classifier.predict(x_test) # %% codecell # Making the Confusion Matrix
# Random Forest Regression # %% codecell # Importing the libraries from sklearn.ensemble import RandomForestRegressor import sys import os import matplotlib.pyplot as plt import numpy as np if '__file__' not in globals(): sys.path.append(os.getcwd() + '/Machine_Learning_A-Z_Mine/Part 2 - Regression/Section 9 - Random Forest Regression') import data_preprocessing_template as preprocessed_data # %% codecell # preprocess data x, y = preprocessed_data.preprocess_data() # %% codecell # Fitting Random Forest Regression to dataset regresor = RandomForestRegressor(criterion="mse", n_estimators=300, random_state=0) regresor.fit(x, y.ravel()) # %% codecell """ # Visualising the Random Forest Regression results plt.scatter(x, y, color='red') plt.plot(x, regresor.predict(x), color='blue') plt.title('Truth or Bluff (Decision Tree Regression)') plt.xlabel('Position level') plt.ylabel('Salary')
# Importing the libraries from sklearn.svm import SVR import sys import os import matplotlib.pyplot as plt import numpy as np if '__file__' not in globals(): sys.path.append( os.getcwd() + '/Machine_Learning_A-Z_Mine/Part 2 - Regression/Section 7 - Support Vector Regression (SVR)' ) import data_preprocessing_template as preprocessed_data # %% codecell # preprocess data x, y, sc_x, sc_y = preprocessed_data.preprocess_data() # %% codecell # Fitting SVR to dataset regresor = SVR(kernel='rbf') regresor.fit(x, y.ravel()) # %% codecell # Visualising the SVR results plt.scatter(x, y, color='red') plt.plot(x, regresor.predict(x), color='blue') plt.title('Truth or Bluff (SVR)') plt.xlabel('Position level') plt.ylabel('Salary') plt.show()