69 lines
1.9 KiB
Python
69 lines
1.9 KiB
Python
#Day 1: Data Prepocessing
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#Step 1: Importing the libraries
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import numpy as np
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import pandas as pd
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#Step 2: Importing dataset
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dataset = pd.read_csv('../datasets/Data.csv')
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X = dataset.iloc[ : , :-1].values
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Y = dataset.iloc[ : , 3].values
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print("Step 2: Importing dataset")
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print("X")
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print(X)
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print("Y")
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print(Y)
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#Step 3: Handling the missing data
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from sklearn.preprocessing import Imputer
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imputer = Imputer(missing_values = "NaN", strategy = "mean", axis = 0)
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imputer = imputer.fit(X[ : , 1:3])
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X[ : , 1:3] = imputer.transform(X[ : , 1:3])
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print("---------------------")
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print("Step 3: Handling the missing data")
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print("step2")
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print("X")
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print(X)
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#Step 4: Encoding categorical data
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from sklearn.preprocessing import LabelEncoder, OneHotEncoder
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labelencoder_X = LabelEncoder()
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X[ : , 0] = labelencoder_X.fit_transform(X[ : , 0])
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#Creating a dummy variable
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onehotencoder = OneHotEncoder(categorical_features = [0])
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X = onehotencoder.fit_transform(X).toarray()
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labelencoder_Y = LabelEncoder()
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Y = labelencoder_Y.fit_transform(Y)
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print("---------------------")
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print("Step 4: Encoding categorical data")
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print("X")
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print(X)
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print("Y")
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print(Y)
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#Step 5: Splitting the datasets into training sets and Test sets
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from sklearn.model_selection import train_test_split
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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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print("---------------------")
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print("Step 5: Splitting the datasets into training sets and Test sets")
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print("X_train")
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print(X_train)
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print("X_test")
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print(X_test)
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print("Y_train")
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print(Y_train)
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print("Y_test")
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print(Y_test)
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#Step 6: Feature Scaling
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from sklearn.preprocessing import StandardScaler
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sc_X = StandardScaler()
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X_train = sc_X.fit_transform(X_train)
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X_test = sc_X.transform(X_test)
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print("---------------------")
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print("Step 6: Feature Scaling")
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print("X_train")
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print(X_train)
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print("X_test")
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print(X_test)
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