diff --git a/guide/english/machine-learning/linear-regression/index.md b/guide/english/machine-learning/linear-regression/index.md index c0daee1752..28209a1d55 100644 --- a/guide/english/machine-learning/linear-regression/index.md +++ b/guide/english/machine-learning/linear-regression/index.md @@ -51,21 +51,28 @@ Apply directly by using scikit library, thus making linear regression easy to us import pandas as pd from sklearn.cross_validation import train_test_split from sklearn.linear_model import LinearRegression as lr +from sklearn import metrics + +# Load the data train = pd.read_csv('../input/train.csv') test = pd.read_csv('../input/test.csv') + +# Separate the features and labels X = train.iloc[:, 0:4].values y = train.iloc[:, 4].values + +# Split the data into train and test X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0) -X_train + +# Build the model model = lr() model.fit(X_train, y_train) -print(model.score(X_train,y_train)) +print("Training score: ", model.score(X_train,y_train)) +print("Gradient: ", model.coef_) +print("y-intercept: ", model.intercept_) + +# Evaluating the model on test data y_pred_class = model.predict(X_test) -model.score(X_train,y_train) -print(model.coef_) -print(model.intercept_) -# calculate accuracy -from sklearn import metrics print(metrics.accuracy_score(y_test, y_pred_class)) ```