神經(jīng)網(wǎng)絡(luò)是高級(jí)的邏輯回歸（這句話沒毛病吧）
在python中使用神經(jīng)網(wǎng)絡(luò)除了sklearn調(diào)包不同以外，其他的原則都是相同。

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

• 導(dǎo)入數(shù)據(jù)

data = pd.read_csv('PDM\\xx\\4.5\\data.csv')

• 取消空值

data=data.dropna()

• 轉(zhuǎn)化文本數(shù)據(jù)為離散型數(shù)值型數(shù)據(jù)

data['Education Level'].unique()

educationLevelDict={
    'Post-Doc':9,
    'Doctorate':8,
    'Master\'s Degree':7,
    'Bachelor\'s Degree':6,
    'Associate\'s Degree':5,
    'Some College':4,
    'Trade School':3,
    'High School':2,
    'Grade School':1
}

dummiesSelect=[
    'Age', 'Num Bathrooms', 'Num Bedrooms', 'Num Cars', 'Num Children', 'Num TVs', 
    'Education Level Map', 'PPV Freq Map', 'Theater Freq Map', 'TV Movie Freq Map', 
    'Prerec Buying Freq Map', 'Prerec Renting Freq Map', 'Prerec Viewing Freq Map', 
    'Gender Male',
    'Internet Connection DSL', 'Internet Connection Dial-Up', 
    'Internet Connection IDSN', 'Internet Connection No Internet Connection',
    'Internet Connection Other', 
    'Marital Status Married', 'Marital Status Never Married', 
    'Marital Status Other', 'Marital Status Separated', 
    'Movie Selector Me', 'Movie Selector Other', 'Movie Selector Spouse/Partner', 
    'Prerec Format DVD', 'Prerec Format Laserdisk', 'Prerec Format Other', 
    'Prerec Format VHS', 'Prerec Format Video CD', 
    'TV Signal Analog antennae', 'TV Signal Cable', 
    'TV Signal Digital Satellite', 'TV Signal Don\'t watch TV'
]

data['Education Level Map'] = data['Education Level'].map(educationLevelDict)

educationLevelDict

data

data['Gender'].unique()

for m in data['Gender'].unique():
    print(m)

type(m)

genderdict={
    'Male':1,
    'Female':0
}

data['Gender Level Map'] = data['Gender'].map(genderdict)

data['Home Ownership'].unique()

HomeOwnershipdict={
    'Own':1,
    'Rent':0
}

data['Home Ownership Level Map'] = data['Home Ownership'].map(HomeOwnershipdict)

data['Internet Connection'].unique()

InternetConnectiondict={
    'Dial-Up':1,
    'DSL':2,
    'Cable Modem':3,
    'No Internet Connection':4,
    'Other':5,
    'IDSN':6
}

data['Internet Connection Level Map'] = data['Internet Connection'].map(InternetConnectiondict)

data['Marital Status'].unique()

MaritalStatusdict={
    'Married':1,
    'Divorced':2,
    'Never Married':3,
    'Separated':4,
    'Other':5
}

data['Marital Status Level Map']=data['Marital Status'].map(MaritalStatusdict)

data['Movie Selector'].unique()

MovieSelectordict={
    'Spouse/Partner':1,
    'Me':2,
    'Other':3,
    'Children':4
}

data['Movie Selector Level Map'] = data['Movie Selector'].map(MovieSelectordict)

data['Prerec Format'].unique()

PrerecFormatdict = {
    'DVD':1,
    'VHS':2,
    'Betamax':3,
    'Laserdisk':4,
    'Video CD':5,
    'Other':6
}

data['Prerec Format Level Map'] = data['Prerec Format'].map(PrerecFormatdict)

data['TV Signal'].unique()

TVSignaldict={
    'Cable':1,
    'Digital Satellite':2,
    'Analog antennae':3,
    'Don\'t watch TV':4,
    'Analog Satellite':5
}

data['TV Signal Level Map'] = data['TV Signal'].map(TVSignaldict)

freqMap={
    'Never':0,
    'Rarely':1,
    'Monthly':2,
    'Weekly':3,
    'Daily':4
}

data['PPV Freq Map'] = data['PPV Freq'].map(freqMap)

data['Theater Freq Map']=data['Theater Freq'].map(freqMap)

data['TV Movie Freq Map']= data['TV Movie Freq'].map(freqMap)

data['Prerec Buying Freq Map']=data['Prerec Buying Freq'].map(freqMap)

data['Prerec Renting Freq Map']= data['Prerec Renting Freq'].map(freqMap)

data['Prerec Viewing Freq Map'] = data['Prerec Viewing Freq'].map(freqMap)

• 建立新的輸入數(shù)據(jù)矩陣（純數(shù)值型數(shù)據(jù)）

dummiesSelect = [
    'Age', 'Num Bathrooms', 'Num Bedrooms', 'Num Cars', 'Num Children', 'Num TVs', 
    'Education Level Map', 'PPV Freq Map', 'Theater Freq Map', 'TV Movie Freq Map', 
    'Prerec Buying Freq Map', 'Prerec Renting Freq Map', 'Prerec Viewing Freq Map', 
    'Gender Level Map',
    'Internet Connection Level Map', 
    'Marital Status Level Map',  
    'Movie Selector Level Map', 
    'Prerec Format Level Map', 
    'TV Signal Level Map'
]

inputData = data[dummiesSelect]

• 建立輸出數(shù)據(jù)矩陣

outputData = data[['Home Ownership Level Map']]

• 導(dǎo)入計(jì)算模型

from sklearn.neural_network import MLPClassifier
for l in range(1, 11):
    ANNModel = MLPClassifier(
        activation='relu', 
        hidden_layer_sizes=l
    )

    ANNModel.fit(inputData, outputData)

    score = ANNModel.score(inputData, outputData)
    print(str(l) + ", " + str(score))

預(yù)測(cè)

• 導(dǎo)入預(yù)測(cè)數(shù)據(jù)

newData = pd.read_csv('PDM\\xx\\4.5\\data.csv')

• 對(duì)預(yù)測(cè)數(shù)據(jù)進(jìn)行預(yù)處理去空值

newData=newData.dropna()

• 建立純數(shù)值輸入矩陣

newData['Education Level Map'] = newData['Education Level'].map(educationLevelDict)

newData['Gender Level Map'] =newData['Gender'].map(genderdict)

newData['Home Ownership Level Map'] = newData['Home Ownership'].map(HomeOwnershipdict)

newData['Internet Connection Level Map'] = newData['Internet Connection'].map(InternetConnectiondict)


newData['Marital Status Level Map']=newData['Marital Status'].map(MaritalStatusdict)


newData['Movie Selector Level Map'] = newData['Movie Selector'].map(MovieSelectordict)

newData['Prerec Format Level Map'] = newData['Prerec Format'].map(PrerecFormatdict)

newData['TV Signal Level Map'] = newData['TV Signal'].map(TVSignaldict)

newData['PPV Freq Map'] = newData['PPV Freq'].map(freqMap)

newData['Theater Freq Map']=newData['Theater Freq'].map(freqMap)

newData['TV Movie Freq Map']= newData['TV Movie Freq'].map(freqMap)

newData['Prerec Buying Freq Map']=newData['Prerec Buying Freq'].map(freqMap)

newData['Prerec Renting Freq Map']= newData['Prerec Renting Freq'].map(freqMap)

newData['Prerec Viewing Freq Map'] = newData['Prerec Viewing Freq'].map(freqMap)
inputNewData= newData[dummiesSelect]

• 預(yù)測(cè)

ANNModel.predict(inputNewData)