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Author: susanli2016

data_preprocessing.py

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+# Import the libraries
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Import the dataset 
+dataset = pd.read_csv('data.csv')
+X = dataset.iloc[:, :-1].values
+y = dataset.iloc[:, 3].values
+
+# Taking care of missing data
+from sklearn.preprocessing import Imputer
+imputer=Imputer(missing_values='NaN', strategy='mean', axis=0)
+imputer=imputer.fit(X[:, 1:3])
+X[:, 1:3] = imputer.transform(X[:, 1:3])
+
+# Encoding categorical data
+from sklearn.preprocessing import LabelEncoder, OneHotEncoder
+labelencoder_X = LabelEncoder()
+X[:,0] = labelencoder_X.fit_transform(X[:,0])
+onehotencoder = OneHotEncoder(categorical_features = [0])
+X = onehotencoder.fit_transform(X).toarray()
+labelencoder_y = LabelEncoder()
+y = labelencoder_y.fit_transform(y)
+
+# 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=42)
+
+# Feature scaling
+from sklearn.preprocessing import StandardScaler
+sc_X = StandardScaler()
+X_train = sc_X.fit_transform(X_train)
+X_test = sc_X.transform(X_test)
+