基于python机器学习对Titanic幸存者进行预测

向右看齐 2022-11-22 00:15 236阅读 0赞

> 发生在1912年的泰坦尼克事件，导致船上2224名游客死亡1502（我们的男主角也牺牲了），作为事后诸葛亮，我们掌握了船上乘客的一些个人信息和一部分乘客是否获救的信息。我们希望能通过探索这些数据，发现一些不为人知的秘密随便预测下另外一部分乘客是否能够获救。下面将用pandas进行数据预处理，用matplotlib进行画图，用sklearn进行模型的训练和预测

###  前期准备： ###

pip install pandas
    pip install numpy
    pip install matplotlib
    pip install sklearn

###  加载数据集并进行数据预处理 ###

def get_csv(path):
        train = pd.read_csv(path, index_col=0)
        print(train.head())
        print("处理前数据总量为：{}".format(len(train)))
        print("含有缺失值数量：\n{}".format(train.isnull().sum()))
        # 缺失值过多 删除cabin列
        train.drop('Cabin', axis=1, inplace=True)
        # 将没有年龄得设置为平均年龄
        train['Age'] = np.where(train['Age'].isnull(), np.mean(train['Age']), train['Age'])
        # 将没有票价得设置为平均数票价
        train['Fare'] = np.where(train['Fare'].isnull(), np.mean(train['Fare']), train['Fare'])
        # 删除没有embrked的两条空数据
        train = train.dropna(subset=["Embarked"])
        print("处理后数据总量为：{}".format(len(train)))
        print("含有缺失值数量：\n{}".format(train.isnull().sum()))
        return train

**数据结构为:**

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQyMDA2NjEz_size_16_color_FFFFFF_t_70][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQyMDA2NjEz_size_16_color_FFFFFF_t_70 1][]

### 数据分析（初步了解数据之间的相关性，为特征选取和模型建立做准备。进行统计学与绘图，分析各参数和存活之间的关系） ###

###                 客舱等级 ###

def create_charts_pclass():
        train = get_csv()
        # 取生存行和等级行
        df1 = train[["Pclass", "Survived"]]
        group = df1.groupby(["Pclass"])  # 按等级分组
        df = group.sum()  # 分组求和
        df['notSurvived'] = group.size() - df['Survived']  # 总数减去活着的人等于私人
        df['survived_rate'] = df['Survived'] / (df['notSurvived'] + df['Survived'])
        # hanshu=np.polyfit(df.index.tolist(),df.survived_rate.tolist(),2)
        print(df)
        plt.rcParams['font.sans-serif'] = ['SimHei']
        plt.scatter(df.index.tolist(), df.survived_rate.tolist(), marker='o')
        plt.plot(df.index.tolist(), df.survived_rate.tolist())
        plt.title("客舱等级和生存率之间的关系")
        plt.xlabel("舱位等级")
        plt.ylabel("生存率")
        plt.show()

**根据训练集首先将客舱等级和生存列提取出来，然后根据客舱等级进行分组后求和，求和结果为存活的人，用存活人数除以总数即可得到不同客舱等级的存活率，根据存活率拟合一条曲线如图所示，可以看到一个简单的线性关系，等级越低，生存率越低**

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQyMDA2NjEz_size_16_color_FFFFFF_t_70 2][]

###               性别 ###

def create_charts_sex():
        train = get_csv()
        df1 = train[['Sex', 'Survived']]
        group = df1.groupby("Sex")
        df = group.sum()
        df['notSurvived'] = group.size() - df['Survived']
        df.plot(kind="bar")
        plt.rcParams['font.sans-serif'] = ['SimHei']
        plt.title("性别生存分析")
        plt.show()

**绘制性别分析柱形图可见男性存活率较低**

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQyMDA2NjEz_size_16_color_FFFFFF_t_70 3][]

###          年龄 ###

def create_charts_age():
        train = get_csv()
        df1 = train[['Age', 'Survived']]
        df1['High2'] = "老年"
        df1.High2[df1.Age < 14] = "儿童"
        df1.High2[(df1.Age <= 22) & (df1.Age > 14)] = "青年"
        df1.High2[(df1.Age <= 34) & (df1.Age > 22)] = "中年"
        df1 = df1[['High2', 'Survived']]
        group = df1.groupby("High2")
        df = group.sum()
        df['notSurvived'] = group.size() - df['Survived']
        df.plot(kind="bar")
        plt.rcParams['font.sans-serif'] = ['SimHei']
        plt.title("不同年龄段生存分析")
        plt.show()

**因年龄数据较为复杂，首先根据年龄将人群分为4种，小于14岁为儿童，小于22岁为青年，小于34岁为中年，大于34岁为老年然后绘制不同年龄段柱形图**

**![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQyMDA2NjEz_size_16_color_FFFFFF_t_70 4][]**

###                上船港口 ###

def create_chart_embraked():
        train = get_csv()
        # 取生存行和等级行
        df1 = train[["Embarked", "Survived"]]
        group = df1.groupby(["Embarked"])  # 按等级分组
        df = group.sum()  # 分组求和
        df['notSurvived'] = group.size() - df['Survived']  # 总数减去活着的人等于私人
        df['survived_rate'] = df['Survived'] / (df['notSurvived'] + df['Survived'])
        print(df)
        plt.rcParams['font.sans-serif'] = ['SimHei']
        plt.scatter(df.index.tolist(), df.survived_rate.tolist(), marker='o')
        plt.plot(df.index.tolist(), df.survived_rate.tolist())
        plt.title("上船的港口编号和生存率之间的关系")
        plt.xlabel("港口编号等级")
        plt.ylabel("生存率")
        plt.show()

**采用和客舱等级相同的分析方法，进行线性拟合，结果如图，生存率随着港口编号C、Q、S依次降低**

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQyMDA2NjEz_size_16_color_FFFFFF_t_70 5][]

###              直系亲友和旁系 ###

def create_chart_sp():
        train = get_csv()
        df1 = train[["Parch", "SibSp", "Survived"]]
        df1['family_size'] = df1['SibSp'] + df1['Parch'] + 1
        df1['family_samll'] = 0
        df1['family_midle'] = 0
        df1['family_big'] = 0
        df1.family_samll[df1.family_size <= 1] = 1
        df1.family_midle[(df1.family_size <= 4) & (df1.family_size >= 2)] = 1
        df1.family_big[df1.family_size > 5] = 1
        df = df1.groupby('family_size').sum()
        print(df)
        df['Survived'].plot()
        plt.rcParams['font.sans-serif'] = ['SimHei']
        plt.title("家庭成员个数和幸存人数")
        plt.show()

**分析方法为将直系亲友和旁及加1总计为一个家庭，根据家庭人数分为大家庭、小家庭和中家庭、根据家庭成员个数绘制与死亡直接的关系图**

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQyMDA2NjEz_size_16_color_FFFFFF_t_70 6][]

### 特征选取（根据上面统计结果图，比对各特征对survived值的影响，选取合适的特征） ###

#性别one-hot编码
        full['Sex'] = np.where(full['Sex'] == "male", 1, 0)
        #pclase one-hot编码
        df_pclass =pd.get_dummies(full['Pclass'],prefix="pclass")
        full = pd.concat([full,df_pclass],axis=1)
        #age one-hot编码
        full['age'] = "老年"
        full.age[full.Age < 14] = "儿童"
        full.age[(full.Age <= 22) & (full.Age > 14)] = "青年"
        full.age[(full.Age <= 34) & (full.Age > 22)] = "中年"
        df_age=pd.get_dummies(full['age'])
        full = pd.concat([full, df_age], axis=1)
        #family one-hot编码
        full['family_size'] = full['SibSp'] + full['Parch'] + 1
        full['family_samll'] = 0
        full['family_midle'] = 0
        full['family_big'] = 0
        full.family_samll[full.family_size <= 1] = 1
        full.family_midle[(full.family_size <= 4) & (full.family_size >= 2)] = 1
        full.family_big[full.family_size > 5] = 1
        #embarked one-hot编码
        df_embarkd =pd.get_dummies(full['Embarked'])
        full = pd.concat([full, df_embarkd], axis=1)
        #删除无用列
        del_list=["Pclass","Name","Age","SibSp","Parch","Ticket","Embarked","age"]
        del full["Name"]
        del full["Age"]
        del full["SibSp"]
        del full["Parch"]
        del full["Ticket"]
        del full["Embarked"]
        del full['age']
        del full["Pclass"]
        # del full["Fare"]
        print(full)

**根据分析将性别，船舱等级，年龄，家庭成员个数，上床的港口编号进行one-hot编码 ，方便后期进行机器学习，在将原始多余的列名进行删除。调用corr方法生成相关性，查看和surivived相关性最强的列名。由图可知，性别，船舱等级，票价，港口编号对survived影响较大，年龄段影响较小**

![20201103104604851.png][]

### 建立模型（选取合适的算法建立预测模型，将测试集带入模型得预测结果）。 ###

**上文已将训练集和测试集进行了合并进行了数据预处理，这里进行一下拆分。889为训练集长度。用LogisticRegression进行模型训练，传入训练集。生成正确率，**

fina_train = full.drop(['Survived'],axis=1)
        print(full)
        len_sour=889
        x=fina_train.loc[0:len_sour-1,:]
        y=full.loc[0:len_sour-1,'Survived']
        pred_x = fina_train.loc[len_sour:,:]
        x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=.8)
        model=LogisticRegression()
        model.fit(x_train,y_train)
        print("正确率为:{}".format(model.score(x_test,y_test)*100))

结果为：

![20201103104748783.png][]

**使用刚才训练好的模型，对测试集进行预测，生成预测结果**

pred_y = model.predict(pred_x)
        pred_y=pred_y.astype(int)
        print(pred_y)

预测结果为：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQyMDA2NjEz_size_16_color_FFFFFF_t_70 7][]

**注：具体代码请参考py文件，文档中部分代码省略，如果需要源码和数据文件，欢迎在评论区留言或者私信我，如果觉得写得还不错，就点个赞吧哈哈哈哈哈哈**

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[20201103104604851.png]: https://img-blog.csdnimg.cn/20201103104604851.png
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