# Import libraries from sklearn.linear_model import LinearRegression # define data, create model and fit data X = Variables Y = Features Model = LinearRegression().fit(X, Y) # Score model Model.score(X, y) # Predict new values NewY = Model.Predict(NewX)
Created on Sun Oct 29 01:16:32 2017 @author: amisha """ import numpy as np import matplotlib.pyplot as plt import pandas as pd #import data set dataset = pd.read_csv('/home/amisha/Desktop/doc.csv') X = dataset.iloc[:, :-1].values y = dataset.iloc[:, 1].values from sklearn.linear_model import LinearRegression regressor = LinearRegression() regressor.fit(X, y) from sklearn.preprocessing import PolynomialFeatures poly_reg = PolynomialFeatures(degree=2) X_poly = poly_reg.fit_transform(X) poly_reg.fit(X_poly, y) lin_reg_2 = LinearRegression() lin_reg_2.fit(X_poly, y) plt.scatter(X, y, color='red') plt.plot(X, LinearRegression.Predict(X), color='blue') plt.title('age vs mortality (Polynomial Regression)') plt.xlabel('age') plt.ylabel('mortality') plt.show()
y = dataset.iloc[:, 4].values # Encoding categorical data from sklearn.preprocessing import LabelEncoder, OneHotEncoder labelencoder = LabelEncoder() X[:, 3] = labelencoder.fit_transform(X[:, 3]) onehotencoder = OneHotEncoder(categorical_features=[3]) X = onehotencoder.fit_transform(X).toarray() #Fitting Multiple Linear Regression to the training set from sklearn.linear_model import LinearRegression regressor = LinearRegression() regressor.fit(X_train) #Predicting the Test set result y_pred = regressor.Predict(X_test) # Avoiding the Dummy Variable Trap X = X[:, 1:] # Splitting the dataset into the Training set and Test set from sklearn.cross_validation import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) #Optimal model with backpropagation import statsmodel.formula.api as sm #ajouter bO X = np.append(arr=np.ones((50, 1)).astype(int), values=X, axis=1)