机器学习案例1---A journey through Titanic

// 1.Imports 引入Python库// pandasimport pandas as pdfrom pandas import Series,DataFrame

//#2. numpy,matplotlib,seabornimport numpy as npimport matplotlib.pyplot as pltimport seaborn as sns  #数据可视化模块sns.set_style('whitegrid')

//#3. machine learning   引入机器学习裤from sklearn.linear_model import LogisticRegressionfrom sklearn.svm import SVC,LinearSVCfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.neighbors import KNeighborsClassifierfrom sklearn.naive_bayes import GaussianNB

#4.get titanic & test csv files as a DataFrame 读取数据titanic_df=pd.read_csv("C://Users//Allen//Desktop//titantic//train.csv")test_df=pd.read_csv("C://Users//Allen//Desktop//titantic//test.csv")

#5.privew data 预览数据titanic_df.head()

  PassengerId  Survived  Pclass  \0            1         0       3   1            2         1       1   2            3         1       3   3            4         1       1   4            5         0       3                                                   Name     Sex   Age  SibSp  \0                            Braund, Mr. Owen Harris    male  22.0      1   1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   2                             Heikkinen, Miss. Laina  female  26.0      0   3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   4                           Allen, Mr. William Henry    male  35.0      0      Parch            Ticket     Fare Cabin Embarked  0      0         A/5 21171   7.2500   NaN        S  1      0          PC 17599  71.2833   C85        C  2      0  STON/O2. 3101282   7.9250   NaN        S  3      0            113803  53.1000  C123        S  4      0            373450   8.0500   NaN        S 

6.字段解释：VARIABLE DESCRIPTIONS:是否生还 survival        Survival                (0 = No; 1 = Yes)等级     pclass          Passenger Class                (1 = 1st; 2 = 2nd; 3 = 3rd)姓名     name            Name性别     sex             Sex年龄     age             Age海外兄弟姐妹数 sibsp           Number of Siblings/Spouses Aboard海外父母/子女数  parch           Number of Parents/Children Aboard机票号   ticket          Ticket Number旅客票价  fare            Passenger Fare舱室  cabin           Cabin着陆地点  embarked        Port of Embarkation                (C = Cherbourg; Q = Queenstown; S = Southampton)

#7.查看数据表信息titanic_df.info()print("---------------------------------")test_df.info()Data columns (total 12 columns):PassengerId    891 non-null int64Survived       891 non-null int64Pclass         891 non-null int64Name           891 non-null objectSex            891 non-null objectAge            714 non-null float64SibSp          891 non-null int64Parch          891 non-null int64Ticket         891 non-null objectFare           891 non-null float64Cabin          204 non-null objectEmbarked       889 non-null object*dtypes: float64(2), int64(5), object(5)memory usage: 83.6+ KB---------------------------------<class 'pandas.core.frame.DataFrame'>RangeIndex: 418 entries, 0 to 417Data columns (total 11 columns):PassengerId    418 non-null int64Pclass         418 non-null int64Name           418 non-null objectSex            418 non-null objectAge            332 non-null float64SibSp          418 non-null int64Parch          418 non-null int64Ticket         418 non-null objectFare           417 non-null float64Cabin          91 non-null objectEmbarked       418 non-null objectdtypes: float64(2), int64(4), object(5)memory usage: 36.0+ KB

# 8.删除非必要字段, 在分析和预测中这些字段不会起作用titanic_df=titanic_df.drop(['PassengerId','Name','Ticket'],axis=1)test_df=test_df.drop(['Name','Ticket'],axis=1)

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# 9.分析各字段---Embarked# only in titanic_df, fill the two missing values with the most occurred # value, which is "S".titanic_df["Embarked"] =titanic_df["Embarked"].fillna("S")# plot 绘图分析Embarked与Survived的关系 size，aspect两个参数用于控制图形大小比例sns.factorplot('Embarked','Survived', data=titanic_df,size=4,aspect=3)

fig, (axis1,axis2,axis3) = plt.subplots(1,3,figsize=(15,5))#sns.factorplot('Embarked',data=titanic_df,kind='count',order=['S','C','Q'],ax=axis1)#sns.factorplot('Survived',hue="Embarked",data=titanic_df,kind='count',order=[1,0],ax=axis2)sns.countplot(x='Embarked', data=titanic_df, ax=axis1)sns.countplot(x='Survived', hue="Embarked", data=titanic_df, order=[1,0], ax=axis2)# group by embarked, and get the mean for survived passengers for each value in Embarkedembark_perc = titanic_df[["Embarked", "Survived"]].groupby(['Embarked'],as_index=False).mean()sns.barplot(x='Embarked', y='Survived', data=embark_perc,order=['S','C','Q'],ax=axis3)

# Either to consider Embarked column in predictions,# and remove "S" dummy variable, # and leave "C" & "Q", since they seem to have a good rate for Survival.# OR, don't create dummy variables for Embarked column, just drop it, # because logically, Embarked doesn't seem to be useful in prediction.embark_dummies_titanic=pd.get_dummies(titanic_df['Embarked'])embark_dummies_titanic.drop(['S'],axis=1,inplace=True)embark_dummies_test  = pd.get_dummies(test_df['Embarked'])embark_dummies_test.drop(['S'], axis=1, inplace=True)titanic_df=titanic_df.join(embark_dummies_titanic)test_df=test_df.join(embark_dummies_test)titanic_df.drop(['Embarked'],axis=1,inplace=True)test_df.drop(['Embarked'],axis=1,inplace=True)

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#Fare（机票价格） 字段分析 缺失值处理# only for test_df, since there is a missing "Fare" valuestest_df["Fare"].fillna(test_df["Fare"].median(),inplace=True)# convert from float to inttitanic_df['Fare']=titanic_df['Fare'].astype(int)test_df['Fare']=test_df['Fare'].astype(int)# get average and std for fare of survived/not survived passengersaverage_fare=DataFrame([fare_not_survived.mean(),fare_survived.mean()])std_fare=DataFrame([fare_not_survived.std(),fare_survived.std()])

#plottitanic_df['Fare'].plot(kind='hist', figsize=(15,3),bins=100, xlim=(0,50))average_fare.index.names = std_fare.index.names = ["Survived"]average_fare.plot(yerr=std_fare,kind='bar',legend=False)average_fare.plot(yerr=average_fare,kind='bar',legend=False)

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# Age 年龄字段分析fig, (axis1,axis2) = plt.subplots(1,2,figsize=(15,4))axis1.set_title('Original Age Values - Titanic')axis2.set_title('New Age Values - Titanic')# axis3.set_title('Original Age values - Test')# axis4.set_title('New Age values - Test')#Agefig, (axis1,axis2) = plt.subplots(1,2,figsize=(15,4))axis1.set_title('Original Age Values - Titanic')axis2.set_title('New Age Values - Titanic')# axis3.set_title('Original Age values - Test')# axis4.set_title('New Age values - Test')# get average, std, and number of NaN values in titanic_dfaverage_age_titanic=titanic_df["Age"].mean()std_age_titanic = titanic_df["Age"].std()count_nan_age_titanic = titanic_df["Age"].isnull().sum()# get average, std, and number of NaN values in test_dfaverage_age_test   = test_df["Age"].mean()std_age_test       = test_df["Age"].std()count_nan_age_test = test_df["Age"].isnull().sum()# generate random numbers between (mean - std) & (mean + std)rand_1=np.random.randint(average_age_titanic - std_age_titanic,average_age_titanic + std_age_titanic)rand_2 = np.random.randint(average_age_test - std_age_test, average_age_test + std_age_test, size = count_nan_age_test)# plot original Age values# NOTE: drop all null values, and convert to inttitanic_df['Age'].dropna().astype(int).hist(bins=70, ax=axis1)# fill NaN values in Age column with random values generatedtitanic_df["Age"][np.isnan(titanic_df["Age"])] = rand_1test_df["Age"][np.isnan(test_df["Age"])] = rand_2# convert from float to inttitanic_df['Age'] = titanic_df['Age'].astype(int)test_df['Age']    = test_df['Age'].astype(int)       # plot new Age Valuestitanic_df['Age'].hist(bins=70, ax=axis2)# test_df['Age'].hist(bins=70, ax=axis4)

#Agefig, (axis1,axis2) = plt.subplots(1,2,figsize=(15,4))axis1.set_title('Original Age Values - Titanic')axis2.set_title('New Age Values - Titanic')# axis3.set_title('Original Age values - Test')# axis4.set_title('New Age values - Test')# get average, std, and number of NaN values in titanic_dfaverage_age_titanic=titanic_df["Age"].mean()std_age_titanic = titanic_df["Age"].std()count_nan_age_titanic = titanic_df["Age"].isnull().sum()# get average, std, and number of NaN values in test_dfaverage_age_test   = test_df["Age"].mean()std_age_test       = test_df["Age"].std()count_nan_age_test = test_df["Age"].isnull().sum()# generate random numbers between (mean - std) & (mean + std)rand_1=np.random.randint(average_age_titanic - std_age_titanic,average_age_titanic + std_age_titanic)rand_2 = np.random.randint(average_age_test - std_age_test, average_age_test + std_age_test, size = count_nan_age_test)# plot original Age values# NOTE: drop all null values, and convert to inttitanic_df['Age'].dropna().astype(int).hist(bins=70, ax=axis1)# fill NaN values in Age column with random values generatedtitanic_df["Age"][np.isnan(titanic_df["Age"])] = rand_1test_df["Age"][np.isnan(test_df["Age"])] = rand_2# convert from float to inttitanic_df['Age'] = titanic_df['Age'].astype(int)test_df['Age']    = test_df['Age'].astype(int)# plot new Age Valuestitanic_df['Age'].hist(bins=70, ax=axis2)# test_df['Age'].hist(bins=70, ax=axis4)

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#Cabin# It has a lot of NaN values, so it won't cause a remarkable impact on prediction 空值较多，需要删掉titanic_df.drop("Cabin",axis=1,inplace=True)test_df.drop("Cabin",axis=1,inplace=True)

#Family# Instead of having two columns Parch & SibSp, # we can have only one column represent if the passenger had any family member aboard or not,# Meaning, if having any family member(whether parent, brother, ...etc) will increase chances of Survival or not.titanic_df['Family']=titanic_df["Parch"]+titanic_df["SibSp"]titanic_df['Family'].loc[titanic_df['Family'] > 0] = 1titanic_df['Family'].loc[titanic_df['Family'] == 0] = 0test_df['Family'] =  test_df["Parch"] + test_df["SibSp"]test_df['Family'].loc[test_df['Family'] > 0] = 1test_df['Family'].loc[test_df['Family'] == 0] = 0# drop Parch & SibSptitanic_df=titanic_df.drop(['SibSp','Parch'],axis=1)test_df=test_df.drop(['SibSp','Parch'],axis=1)#plotfig,(axis1,axis2)=plt.subplots(1,2,sharex=True,figsize=(10,5))#sns.factorplot('Family',data=titanic_df,kind='count',ax=axis1)sns.countplot(x='Family', data=titanic_df, order=[1,0], ax=axis1)# average of survived for those who had/didn't have any family memberfamily_perc = titanic_df[["Family", "Survived"]].groupby(['Family'],as_index=False).mean()sns.barplot(x='Family', y='Survived', data=family_perc, order=[1,0], ax=axis2)axis1.set_xticklabels(["With Family","Alone"], rotation=0)

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#Sex#Sex# As we see, children(age < ~16) on aboard seem to have a high chances for Survival.# So, we can classify passengers as males, females, and childdef get_person(passenger):    age,sex=passenger    return 'child' if age<16 else sextitanic_df['Person']=titanic_df[['Age','Sex']].apply(get_person,axis=1)test_df['Person']=test_df[['Age','Sex']].apply(get_person,axis=1)# No need to use Sex column since we created Person columntitanic_df.drop(['Sex'],axis=1,inplace=True)test_df.drop(['Sex'],axis=1,inplace=True)# create dummy variables for Person column, & drop Male as it has the lowest average of survived passengersperson_dummies_titanic=pd.get_dummies(titanic_df['Person'])person_dummies_titanic.columns = ['Child','Female','Male']person_dummies_titanic.drop(['Male'], axis=1, inplace=True)person_dummies_test  = pd.get_dummies(test_df['Person'])person_dummies_test.columns = ['Child','Female','Male']person_dummies_test.drop(['Male'], axis=1, inplace=True)titanic_df=titanic_df.join(person_dummies_titanic)test_df=test_df.join(person_dummies_test)fig, (axis1,axis2) = plt.subplots(1,2,figsize=(10,5))# sns.factorplot('Person',data=titanic_df,kind='count',ax=axis1)sns.countplot(x='Person', data=titanic_df, ax=axis1)# average of survived for each Person(male, female, or child)person_perc = titanic_df[["Person", "Survived"]].groupby(['Person'],as_index=False).mean()sns.barplot(x='Person', y='Survived', data=person_perc, ax=axis2, order=['male','female','child'])titanic_df.drop(['Person'],axis=1,inplace=True)test_df.drop(['Person'],axis=1,inplace=True)

#PClass# sns.factorplot('Pclass',data=titanic_df,kind='count',order=[1,2,3])sns.factorplot('Pclass','Survived',order=[1,2,3], data=titanic_df,size=5)# create dummy variables for Pclass column, & drop 3rd class as it has the lowest average of survived passengerspclass_dummies_titanic  = pd.get_dummies(titanic_df['Pclass'])pclass_dummies_titanic.columns = ['Class_1','Class_2','Class_3']pclass_dummies_titanic.drop(['Class_3'], axis=1, inplace=True)pclass_dummies_test  = pd.get_dummies(test_df['Pclass'])pclass_dummies_test.columns = ['Class_1','Class_2','Class_3']pclass_dummies_test.drop(['Class_3'], axis=1, inplace=True)titanic_df.drop(['Pclass'],axis=1,inplace=True)test_df.drop(['Pclass'],axis=1,inplace=True)titanic_df = titanic_df.join(pclass_dummies_titanic)test_df    = test_df.join(pclass_dummies_test)

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#define train and testing setsX_train=titanic_df.drop("Survived",axis=1)Y_train=titanic_df["Survived"]X_test=test_df.drop("PassengerId",axis=1).copy()

# Logistic Regressionlogreg=LogisticRegression()logreg.fit(X_train,Y_train)Y_pred=logreg.predict(X_test)logreg.score(X_train,Y_train)   // 0.8058361391694725

# Support Vector Machinessvc = SVC()svc.fit(X_train, Y_train)Y_pred = svc.predict(X_test)svc.score(X_train, Y_train)//0.85746352413019078

#Random Forestrandom_forest=RandomForestClassifier(n_estimators=100)random_forest.fit(X_train,Y_train)Y_pred=random_forest.predict(X_test)random_forest.score(X_train,Y_train)//0.96296296296296291

#knnknn=KNeighborsClassifier(n_neighbors=3)knn.fit(X_train,Y_train)Y_pred=knn.predict(X_test)knn.score(X_train,Y_train)//0.84062850729517391

# Gaussian Naive Bayesgaussian=GaussianNB()gaussian.fit(X_train,Y_train)Y_pred=gaussian.predict(X_test)gaussian.score(X_train,Y_train)//0.76094276094276092

# get Correlation Coefficient for each feature using Logistic Regressioncoeff_df=DataFrame(titanic_df.columns.delete(0))coeff_df.columns=['Features']coeff_df["Coefficient Estimate"] = pd.Series(logreg.coef_[0])//previewcoeff_df//Features  Coefficient Estimate//0      Age             -0.027647//1     Fare              0.000811//2        C              0.604322//3        Q              0.312577//4   Family             -0.274143//5    Child              1.779651//6   Female              2.774104//7  Class_1              2.060579//8  Class_2              1.126502