Kaggle经典测试，泰坦尼克号的生存预测，机器学习实验----02

男娘i 2021-09-09 09:08 426阅读 0赞

## Kaggle经典测试，泰坦尼克号的生存预测，机器学习实验----02 ##

### 文章目录 ###

*   *  Kaggle经典测试，泰坦尼克号的生存预测，机器学习实验----02
     *   *  一、引言
         *  二、问题
         *  三、问题分析
         *  四、具体操作
         *   *  1、读取数据并且进行预处理
             *  2、划分标签以及特征并且初始化参数
             *  3、开始线性回归
             *  4、测试模型并且进行打分
             *  5、保存数据结果
         *  五、完整代码
         *  六、本算法的核心代码：

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L20wXzU0MjE4MjYz_size_16_color_FFFFFF_t_70_pic_center]

### 一、引言 ###

泰坦尼克号（RMS Titanic），又译作铁达尼号，是英国白星航运公司下辖的一艘奥林匹克级游轮，排水量46000吨，泰坦尼克号是当时世界上体积最庞大、内部设施最豪华的客运轮船，有“永不沉没”的美誉 。

然而不幸的是，在它的处女航中，泰坦尼克号便遭厄运——它从英国南安普敦出发驶向美国纽约。1912年4月14日23时40分左右，泰坦尼克号与一座冰山相撞，造成右舷船艏至船中部破裂，五间水密舱进水。4月15日凌晨2时20分左右，泰坦尼克船体断裂成两截后沉入大西洋底3700米处。2224名船员及乘客中，1517人丧生，其中仅333具罹难者遗体被寻回。泰坦尼克号沉没事故为和平时期死伤人数最为惨重的一次海难，其残骸直至1985年才被再度发现，目前受到联合国教育、科学及文化组织的保护。

### 二、问题 ###

那么，问题来了，想在泰坦尼克号这次灾难中存活下来需要具备那些条件呢？

也就是说，如果实现知道一个人的所有情况，我们该如何判断这个人是否会遇难呢？

这样就需要机器学习来解决了。

### 三、问题分析 ###

显然，在泰坦尼克号这次灾难中，一个人要么是遇难，要么是幸存，所以实际上是一个逻辑回归，但由于这是刚刚起步的一个实验，我们暂且不使用逻辑回归，依然采用线性回归来进行数据的处理和分析，后面我们会再次做这个实验，届时，我们将使用逻辑回归。然而本次就是用线性回归了啦。

### 四、具体操作 ###

#### 1、读取数据并且进行预处理 ####

首先读入文件（csv），然后删除一些不必要的数据，接下来进行一定的调整，既可以得到下面的结果：

def read_data_of_csv(file_name):
        """ read the csv files to get the data of the titanic accident :param file_name: the name of the file :return: df -> the data in the file that is opened above """
        df = pandas.read_csv(file_name)
        return df
    
    
    if __name__ == '__main__':
        """ main """
    
        # here we need not to split the train and the test !
    
        """ 1.get data and do the prior things before the machine learning """
        df_train = read_data_of_csv("titanic/train.csv")
        # print(df_train)
        # deal with the data first before machine learning
        df_train.drop("Embarked", axis=1, inplace=True)
        # i think embarked is not useful, so i delete this embarked line
        df_train.drop("Cabin", axis=1, inplace=True)
        # delete the cabin
        df_train.drop("Ticket", axis=1, inplace=True)
        # delete the ticket
        df_train.drop("Name", axis=1, inplace=True)
        # delete the name
        df_train.drop("PassengerId", axis=1, inplace=True)
        # delete the passenger id
    
        for int_number_of_len in range(len(df_train)):
            if df_train.loc[int_number_of_len, "Sex"] == "male":
                df_train.loc[int_number_of_len, "Sex"] = 1
                # if male then set the sex 1
            else:
                df_train.loc[int_number_of_len, "Sex"] = 0
                # if female then set the sex 0
        # change the introduction of sex from string to int 1 or 0
        df_train.dropna(axis=0, how="any", inplace=True)
        # delete the NaN data
        print(df_train)
        # show the result

最终的df\_train:

Survived  Pclass Sex   Age  SibSp  Parch     Fare
    0           0       3   1  22.0      1      0   7.2500
    1           1       1   0  38.0      1      0  71.2833
    2           1       3   0  26.0      0      0   7.9250
    3           1       1   0  35.0      1      0  53.1000
    4           0       3   1  35.0      0      0   8.0500
    ..        ...     ...  ..   ...    ...    ...      ...
    885         0       3   0  39.0      0      5  29.1250
    886         0       2   1  27.0      0      0  13.0000
    887         1       1   0  19.0      0      0  30.0000
    889         1       1   1  26.0      0      0  30.0000
    890         0       3   1  32.0      0      0   7.7500
    
    [714 rows x 7 columns]
    
    Process finished with exit code 0

#### 2、划分标签以及特征并且初始化参数 ####

需要将数据中的特征以及标签分开来进行处理

随后需要进行参数的初始化

""" 2.split the label and the features """
        
        y_train = df_train.loc[:, "Survived"]
        print(y_train)
        # y of the train
    
        X_train = df_train.loc[:, "Pclass": "Fare"]
        print(X_train)
        # X of the train
    
        """ 3.set the initial params of the liner regression """
        alpha = float(input("input the alpha:\n"))
    
    
        list_theta = []
        for number_of_the_total_thetas_list in range(6 + 1):
            list_theta[number_of_the_total_thetas_list] = float(
                input(f"input the theta { number_of_the_total_thetas_list}:\n"))
            # input the theta

#### 3、开始线性回归 ####

这里是训练机器的代码

必须强调一下！！

**参数选取非常重要！！！！**

""" 4.make the machine learning of the liner regression operations """
        iter_of_regression = int(input("input the number of iter times:\n"))
    
        for num_of_iter_of_regression in range(iter_of_regression):
            # make iter_of_regression times of the regression
            h_x = list_theta[0] + \
                  list_theta[1] * X_train.loc[:, "Pclass"] + \
                  list_theta[2] * X_train.loc[:, "Sex"] + \
                  list_theta[3] * X_train.loc[:, "Age"] + \
                  list_theta[4] * X_train.loc[:, "SibSp"] + \
                  list_theta[5] * X_train.loc[:, "Parch"] + \
                  list_theta[6] * X_train.loc[:, "Fare"]
            # 7 theta
            # 6 x of the feature
    
            loss = \
                y_train - h_x
            # calculate the loss
    
            # loss ^ 2
            print(loss.T.dot(loss))
    
    
            # the sum of loss:
            sum_loss = 0
            # plus the loss
            for o in range(len(loss)):
                # print(loss.iloc[o])
                sum_loss += loss.iloc[o]  # float
            # list_theta[0] = list_theta[0] + alpha * sum_loss / len(loss)
            # print(loss)
            list_theta[0] += \
                alpha * sum_loss / len(loss)
            # update the list_theta[0]
    
            # print(list(X_train.index))
            # 0's index
            for c in [1, 2, 3, 4, 5, 6]:
                list_theta[c] += \
                    alpha * (X_train.loc[:, list(X_train)[(c - 1)]].T.dot(loss)) / len(loss)
            # update the list theta of the params
            # X_train.loc[:, c - 1].T.dot(loss)
            # T transfer, dot dot_multiply
    
            # do all the thetas !!
    
            # continue
    
            print(list_theta)
            # theta
            # print(loss.T.dot(loss))
            # print(loss.T.dot(loss))
            # loss ^ 2
    
            continue

#### 4、测试模型并且进行打分 ####

""" 5.do the test of this project """
    
        # the same operation
        df_test = read_data_of_csv("titanic/test.csv")
        df_test.drop("Embarked", axis=1, inplace=True)
        df_test.drop("Cabin", axis=1, inplace=True)
        df_test.drop("Ticket", axis=1, inplace=True)
        df_test.drop("Name", axis=1, inplace=True)
        df_test.drop("PassengerId", axis=1, inplace=True)
        # delete
    
        for int_number_of_len in range(len(df_test)):
            if df_test.loc[int_number_of_len, "Sex"] == "male":
                df_test.loc[int_number_of_len, "Sex"] = 1
                # if male then set the sex 1
            else:
                df_test.loc[int_number_of_len, "Sex"] = 0
                # if female then set the sex 0
        # change the introduction of sex from string to int 1 or 0
    
        # delete the NaNs
        df_test.dropna(axis=0, how="any", inplace=True)
        # delete the NaN data
    
        print(df_test)
        # show the result
    
        y_test = df_test.loc[:, "Survived"]
    
        X_test = df_test.loc[:, "Pclass":"Fare"]
    
        print(y_test)
        print(X_test)
    
        test_h_x = list_theta[0] + \
            list_theta[1] * X_train.loc[:, "Pclass"] + \
            list_theta[2] * X_train.loc[:, "Sex"] + \
            list_theta[3] * X_train.loc[:, "Age"] + \
            list_theta[4] * X_train.loc[:, "SibSp"] + \
            list_theta[5] * X_train.loc[:, "Parch"] + \
            list_theta[6] * X_train.loc[:, "Fare"]
        # the final function
    
        """ 6.score the model """
    
        N = len(y_test)
        # the total of the test number
        num_of_win_the_prediction_of_the_model = 0
        # predict the result
    
        # as long as we are right, whether the person is alive or dead, it does not matter
        for win_number_of_the_test_of_each in range(N):
            if test_h_x.iloc[win_number_of_the_test_of_each] < 0.5:
                test_h_x.iloc[win_number_of_the_test_of_each] = 0
                # prediction < 0.5 => 0
                if y_test.iloc[win_number_of_the_test_of_each] == 0:
                    num_of_win_the_prediction_of_the_model += 1
                    # right!
                    # right, so we num of win ++
                else:
                    pass
                    # wrong
    
            else:
                test_h_x.iloc[win_number_of_the_test_of_each] = 1
                # prediction >= 0.5 => 1
                if y_test.iloc[win_number_of_the_test_of_each] == 1:
                    num_of_win_the_prediction_of_the_model += 1
                    # right
                else:
                    pass
                    # wrong

#### 5、保存数据结果 ####

我们打开一个txt文档，将数据保存在里面。

""" 7.save the model """
    
        with open("result.txt", "w") as f:
            f.write("result record:\n")
            # result
    
            f.write("the alpha:\n")
            f.write(f"{ alpha}")
            f.write("\n")
            # 1.alpha
    
            f.write("the thetas of the model:\n")
            recording_number_position = 1
            for theta_of_the_last in list_theta:
                f.write(f"{ recording_number_position}. ")
                f.write(f"{ theta_of_the_last}")
                f.write("\n")
                recording_number_position += 1
                continue
            # 2.write the thetas
            f.write("\n")
    
            f.write("features:\n")
            r_n_p = 1
            for data_of_feature in list(X_train):
                f.write(f"{ r_n_p}. ")
                f.write(f"{ data_of_feature}")
                f.write("\n")
                r_n_p += 1
                continue
            f.write("\n")
            # 3.features
    
            f.write("score:\n")
            f.write(f"{ num_of_win_the_prediction_of_the_model / N}")
            f.write("\n")
            f.write(f" or the 100 :{ 100 * num_of_win_the_prediction_of_the_model / N}")
            f.write("\n")
            # 4.score
    
            f.close()
            # close the file
    
        sys.exit("bye bye!")
    """ END """

最后的数据的呈现：

result record:
    the alpha:
    0.0001
    the thetas of the model:
    1. 1.2486050331704237
    2. -0.16522774554911307
    3. -0.48151480883845743
    4. -0.00541835063447703
    5. -0.04947303449597998
    6. -0.011060136497625706
    7. 0.0005749482239116638
    
    features:
    1. Pclass
    2. Sex
    3. Age
    4. SibSp
    5. Parch
    6. Fare
    
    score:
    0.4954682779456193
      or the 100 :49.546827794561935

从上面的数据可以看出来呢，这个模型并不是很好，以至于连及格都没有及格了啦，wwww~~

不过没有关系，后面我们会使用逻辑回归再做一次这个案例的啦，后面那个显然会好一点哦。

### 五、完整代码 ###

""" the titanic survival prediction of machine learning by author: Hu Yu Xuan at time: 2021/8/9 using method: liner regression """
    
    
    import numpy
    import pandas
    import sys
    
    
    def read_data_of_csv(file_name):
        """ read the csv files to get the data of the titanic accident :param file_name: the name of the file :return: df -> the data in the file that is opened above """
        df = pandas.read_csv(file_name)
        return df
    
    
    if __name__ == '__main__':
        """ main """
    
        # here we need not to split the train and the test !
    
        """ 1.get data and do the prior things before the machine learning """
    
        df_train = read_data_of_csv("titanic/train.csv")
        # print(df_train)
        # deal with the data first before machine learning
        df_train.drop("Embarked", axis=1, inplace=True)
        # i think embarked is not useful, so i delete this embarked line
        df_train.drop("Cabin", axis=1, inplace=True)
        # delete the cabin
        df_train.drop("Ticket", axis=1, inplace=True)
        # delete the ticket
        df_train.drop("Name", axis=1, inplace=True)
        # delete the name
        df_train.drop("PassengerId", axis=1, inplace=True)
        # delete the passenger id
    
        for int_number_of_len in range(len(df_train)):
            if df_train.loc[int_number_of_len, "Sex"] == "male":
                df_train.loc[int_number_of_len, "Sex"] = 1
                # if male then set the sex 1
            else:
                df_train.loc[int_number_of_len, "Sex"] = 0
                # if female then set the sex 0
        # change the introduction of sex from string to int 1 or 0
        df_train.dropna(axis=0, how="any", inplace=True)
        # delete the NaN data
        print(df_train)
        # show the result
    
        """ 2.split the label and the features """
    
        y_train = df_train.loc[:, "Survived"]
        print(y_train)
        # y of the train
    
        X_train = df_train.loc[:, "Pclass": "Fare"]
        print(X_train)
        # X of the train
    
        """ 3.set the initial params of the liner regression """
    
        alpha = float(input("input the alpha:\n"))
        # alpha
        list_theta = [0, 0, 0, 0, 0, 0, 0]
        # 7 params
        for number_of_the_total_thetas_list in range(6 + 1):
            list_theta[number_of_the_total_thetas_list] = float(
                input(f"input the theta { number_of_the_total_thetas_list}:\n"))
            # input the theta
    
        """ 4.make the machine learning of the liner regression operations """
    
        iter_of_regression = int(input("input the number of iter times:\n"))
    
        for num_of_iter_of_regression in range(iter_of_regression):
            # make iter_of_regression times of the regression
            h_x = list_theta[0] + \
                  list_theta[1] * X_train.loc[:, "Pclass"] + \
                  list_theta[2] * X_train.loc[:, "Sex"] + \
                  list_theta[3] * X_train.loc[:, "Age"] + \
                  list_theta[4] * X_train.loc[:, "SibSp"] + \
                  list_theta[5] * X_train.loc[:, "Parch"] + \
                  list_theta[6] * X_train.loc[:, "Fare"]
            # 7 theta
            # 6 x of the feature
    
            loss = \
                y_train - h_x
            # calculate the loss
    
            # loss ^ 2
            print(loss.T.dot(loss))
    
    
            # the sum of loss:
            sum_loss = 0
            # plus the loss
            for o in range(len(loss)):
                # print(loss.iloc[o])
                sum_loss += loss.iloc[o]  # float
            # list_theta[0] = list_theta[0] + alpha * sum_loss / len(loss)
            # print(loss)
            list_theta[0] += \
                alpha * sum_loss / len(loss)
            # update the list_theta[0]
    
            # print(list(X_train.index))
            # 0's index
            for c in [1, 2, 3, 4, 5, 6]:
                list_theta[c] += \
                    alpha * (X_train.loc[:, list(X_train)[(c - 1)]].T.dot(loss)) / len(loss)
            # update the list theta of the params
            # X_train.loc[:, c - 1].T.dot(loss)
            # T transfer, dot dot_multiply
    
            # do all the thetas !!
    
            # continue
    
            print(list_theta)
            # theta
            # print(loss.T.dot(loss))
            # print(loss.T.dot(loss))
            # loss ^ 2
    
            continue
    # 103.57666895852172
    # [1.2448209211390562,
        # -0.164299592246984,
        # -0.48128889342546577,
        # -0.005382484123675659,
        # -0.04934860676751743,
        # -0.01102572287455978,
        # 0.0005836091240344807]
    # 103.57666895852172 --- loss ^ 2
    # [1.2448209211390562, -0.164299592246984, -0.48128889342546577, -0.005382484123675659, -0.04934860676751743, -0.01102572287455978, 0.0005836091240344807]
    
        """ 5.do the test of this project """
    
        # the same operation
        df_test = read_data_of_csv("titanic/test.csv")
        df_test.drop("Embarked", axis=1, inplace=True)
        df_test.drop("Cabin", axis=1, inplace=True)
        df_test.drop("Ticket", axis=1, inplace=True)
        df_test.drop("Name", axis=1, inplace=True)
        df_test.drop("PassengerId", axis=1, inplace=True)
        # delete
    
        for int_number_of_len in range(len(df_test)):
            if df_test.loc[int_number_of_len, "Sex"] == "male":
                df_test.loc[int_number_of_len, "Sex"] = 1
                # if male then set the sex 1
            else:
                df_test.loc[int_number_of_len, "Sex"] = 0
                # if female then set the sex 0
        # change the introduction of sex from string to int 1 or 0
    
        # delete the NaNs
        df_test.dropna(axis=0, how="any", inplace=True)
        # delete the NaN data
    
        print(df_test)
        # show the result
    
        y_test = df_test.loc[:, "Survived"]
    
        X_test = df_test.loc[:, "Pclass":"Fare"]
    
        print(y_test)
        print(X_test)
    
        test_h_x = list_theta[0] + \
            list_theta[1] * X_train.loc[:, "Pclass"] + \
            list_theta[2] * X_train.loc[:, "Sex"] + \
            list_theta[3] * X_train.loc[:, "Age"] + \
            list_theta[4] * X_train.loc[:, "SibSp"] + \
            list_theta[5] * X_train.loc[:, "Parch"] + \
            list_theta[6] * X_train.loc[:, "Fare"]
        # the final function
    
        """ 6.score the model """
    
        N = len(y_test)
        # the total of the test number
        num_of_win_the_prediction_of_the_model = 0
        # predict the result
    
        # as long as we are right, whether the person is alive or dead, it does not matter
        for win_number_of_the_test_of_each in range(N):
            if test_h_x.iloc[win_number_of_the_test_of_each] < 0.5:
                test_h_x.iloc[win_number_of_the_test_of_each] = 0
                # prediction < 0.5 => 0
                if y_test.iloc[win_number_of_the_test_of_each] == 0:
                    num_of_win_the_prediction_of_the_model += 1
                    # right!
                    # right, so we num of win ++
                else:
                    pass
                    # wrong
    
            else:
                test_h_x.iloc[win_number_of_the_test_of_each] = 1
                # prediction >= 0.5 => 1
                if y_test.iloc[win_number_of_the_test_of_each] == 1:
                    num_of_win_the_prediction_of_the_model += 1
                    # right
                else:
                    pass
                    # wrong
    
        """ 7.save the model """
    
        with open("result.txt", "w") as f:
            f.write("result record:\n")
            # result
    
            f.write("the alpha:\n")
            f.write(f"{ alpha}")
            f.write("\n")
            # 1.alpha
    
            f.write("the thetas of the model:\n")
            recording_number_position = 1
            for theta_of_the_last in list_theta:
                f.write(f"{ recording_number_position}. ")
                f.write(f"{ theta_of_the_last}")
                f.write("\n")
                recording_number_position += 1
                continue
            # 2.write the thetas
            f.write("\n")
    
            f.write("features:\n")
            r_n_p = 1
            for data_of_feature in list(X_train):
                f.write(f"{ r_n_p}. ")
                f.write(f"{ data_of_feature}")
                f.write("\n")
                r_n_p += 1
                continue
            f.write("\n")
            # 3.features
    
            f.write("score:\n")
            f.write(f"{ num_of_win_the_prediction_of_the_model / N}")
            f.write("\n")
            f.write(f" or the 100 :{ 100 * num_of_win_the_prediction_of_the_model / N}")
            f.write("\n")
            # 4.score
    
            f.close()
            # close the file
    
        sys.exit("bye bye!")
    """ END """

刚开始，我以为我的这个实验是这样的：

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L20wXzU0MjE4MjYz_size_16_color_FFFFFF_t_70_pic_center 1]

后来做完以后结果是这样的：  
（彻底翻车了，不及格）  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L20wXzU0MjE4MjYz_size_16_color_FFFFFF_t_70_pic_center 2]  
好在这个是我的第二次实验，缺乏一些经验，而且选取的模型也不够合适，导致了这样的偏差， 后面我在做一个逻辑回归，一定会好很多的啦。

### 六、本算法的核心代码： ###

进行参数值的更新：  
（注意更新时的具体代码！！）

# make iter_of_regression times of the regression
            h_x = list_theta[0] + \
                  list_theta[1] * X_train.loc[:, "Pclass"] + \
                  list_theta[2] * X_train.loc[:, "Sex"] + \
                  list_theta[3] * X_train.loc[:, "Age"] + \
                  list_theta[4] * X_train.loc[:, "SibSp"] + \
                  list_theta[5] * X_train.loc[:, "Parch"] + \
                  list_theta[6] * X_train.loc[:, "Fare"]
            # 7 theta
            # 6 x of the feature
    
            loss = \
                y_train - h_x
            # calculate the loss
    
            # loss ^ 2
            print(loss.T.dot(loss))
    
    
            # the sum of loss:
            sum_loss = 0
            # plus the loss
            for o in range(len(loss)):
                # print(loss.iloc[o])
                sum_loss += loss.iloc[o]  # float
            # list_theta[0] = list_theta[0] + alpha * sum_loss / len(loss)
            # print(loss)
            list_theta[0] += \
                alpha * sum_loss / len(loss)
            # update the list_theta[0]
    
            # print(list(X_train.index))
            # 0's index
            for c in [1, 2, 3, 4, 5, 6]:
                list_theta[c] += \
                    alpha * (X_train.loc[:, list(X_train)[(c - 1)]].T.dot(loss)) / len(loss)
            # update the list theta of the params
            # X_train.loc[:, c - 1].T.dot(loss)
            # T transfer, dot dot_multiply
    
            # do all the thetas !!
    
            # continue
    
            print(list_theta)
            # theta
            # print(loss.T.dot(loss))
            # print(loss.T.dot(loss))
            # loss ^ 2

就写到这里啦。

后续可以在来看看我的逻辑回归，这个会更好的啦。

谢谢阅读了了啊。

如果感兴趣的话就点个赞支持一下呗！！！！

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L20wXzU0MjE4MjYz_size_16_color_FFFFFF_t_70_pic_center]: /images/20210813/3cf6246d88cc4437af917496ee917b12.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L20wXzU0MjE4MjYz_size_16_color_FFFFFF_t_70_pic_center 1]: /images/20210813/3f8e74c285954b41a1de72f447021dc6.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L20wXzU0MjE4MjYz_size_16_color_FFFFFF_t_70_pic_center 2]: /images/20210813/ce7ee71c797d44c7ae80e568a0badfaf.png