edit(guide): restructuring the sample code (#29654)
Add some comment, edit some line of codes to make it easier to understand.
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Randell Dawson
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@ -51,21 +51,28 @@ Apply directly by using scikit library, thus making linear regression easy to us
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import pandas as pd
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import pandas as pd
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from sklearn.cross_validation import train_test_split
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from sklearn.cross_validation import train_test_split
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from sklearn.linear_model import LinearRegression as lr
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from sklearn.linear_model import LinearRegression as lr
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from sklearn import metrics
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# Load the data
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train = pd.read_csv('../input/train.csv')
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train = pd.read_csv('../input/train.csv')
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test = pd.read_csv('../input/test.csv')
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test = pd.read_csv('../input/test.csv')
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# Separate the features and labels
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X = train.iloc[:, 0:4].values
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X = train.iloc[:, 0:4].values
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y = train.iloc[:, 4].values
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y = train.iloc[:, 4].values
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# Split the data into train and test
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
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X_train
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# Build the model
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model = lr()
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model = lr()
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model.fit(X_train, y_train)
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model.fit(X_train, y_train)
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print(model.score(X_train,y_train))
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print("Training score: ", model.score(X_train,y_train))
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print("Gradient: ", model.coef_)
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print("y-intercept: ", model.intercept_)
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# Evaluating the model on test data
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y_pred_class = model.predict(X_test)
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y_pred_class = model.predict(X_test)
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model.score(X_train,y_train)
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print(model.coef_)
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print(model.intercept_)
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# calculate accuracy
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from sklearn import metrics
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print(metrics.accuracy_score(y_test, y_pred_class))
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print(metrics.accuracy_score(y_test, y_pred_class))
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```
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```
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