edit(guide): restructuring the sample code (#29654)

Add some comment, edit some line of codes to make it easier to understand.
This commit is contained in:
Varian Caesar
2019-01-31 04:58:17 +07:00
committed by Randell Dawson
parent df1eff0d9f
commit 6e80bb3c79

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@ -51,21 +51,28 @@ Apply directly by using scikit library, thus making linear regression easy to us
import pandas as pd import pandas as pd
from sklearn.cross_validation import train_test_split from sklearn.cross_validation import train_test_split
from sklearn.linear_model import LinearRegression as lr from sklearn.linear_model import LinearRegression as lr
from sklearn import metrics
# Load the data
train = pd.read_csv('../input/train.csv') train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv') test = pd.read_csv('../input/test.csv')
# Separate the features and labels
X = train.iloc[:, 0:4].values X = train.iloc[:, 0:4].values
y = train.iloc[:, 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, 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 = lr()
model.fit(X_train, y_train) 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) 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)) print(metrics.accuracy_score(y_test, y_pred_class))
``` ```