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  • 开始机器学习100天计划

    听说github上有人号召100天内通过项目入门机器学习,觉得是一个特别好的想法。我也一直筹谋着下一份工作一定要是机器学习相关,从web到AI的转型可能是我至今为止面临的最大的难题。但是我不怕,俗话说的好:竹杖芒鞋轻胜马,谁怕,一蓑烟雨任平生。

    第一天: 数据预处理

    # 1. importing the required libraries.
    import numpy as np
    import pandas as pd
    # 2. importing the dataset
    dataset = pd.read_csv("../data/Data.csv")
    X = dataset.iloc[:,:-1].values
    Y = dataset.iloc[:,-1].values
    print(X)
    print(Y)
    # 3. handing the 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])
    print(X)
    # 4. Encoding Categorical Data
    from sklearn.preprocessing import LabelEncoder, OneHotEncoder
    labelecoder_X = LabelEncoder()
    X[: , 0] = labelecoder_X.fit_transform(X[:, 0])
    print(X)
    # 4.1 creating a dummy variable
    onehotencoder = OneHotEncoder(categorical_features = [0])
    X = onehotencoder.fit_transform(X).toarray()
    print(X)
    labelencoder_Y = LabelEncoder()
    Y = labelencoder_Y.fit_transform(Y)
    print(Y)
    # 5. Splitting the dataset into test set and training 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 = 0)
    print(X_train)
    print(X_test)
    print(Y_train)
    print(Y_test)
    # 6. Feature scaling
    from sklearn.preprocessing import StandardScaler
    sc_X = StandardScaler()
    X_train = sc_X.fit_transform(X_train)
    X_test = sc_X.fit_transform(X_test)
    
    print(X_train)
    print(X_test)
    

    Data.csv

    Country,Age,Salary,Purchased
    France,44,72000,No
    Spain,27,48000,Yes
    Germany,30,54000,No
    Spain,38,61000,No
    Germany,40,,Yes
    France,35,58000,Yes
    Spain,,52000,No
    France,48,79000,Yes
    Germany,50,83000,No
    France,37,67000,Yes
    
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  • 原文地址:https://www.cnblogs.com/xiaoxuanzhi/p/9619698.html
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