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    • Kaggle_Titanic
      머신러닝(MACHINE LEARNING) 2021. 4. 9. 00:44
      반응형
      import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
      test_df = pd.read_csv("./test.csv")
train_df = pd.read_csv("./train.csv")
      train_df.head()

      PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
      0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S
      1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C
      2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S
      3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S
      4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S 
      train_df.set_index('PassengerId', inplace=True)
test_df.set_index('PassengerId', inplace=True)
      train_index = train_df.index
test_index = test_df.index
train_index
      Int64Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,
            ...
            882, 883, 884, 885, 886, 887, 888, 889, 890, 891],
           dtype='int64', name='PassengerId', length=891)
      y_train_df = train_df.pop("Survived")

      Data _ Processing 작업

      pd.set_option('display.float_format', lambda x: '%.2f'%x)
      test_df.isnull().sum()
      Pclass        0
Name          0
Sex           0
Age          86
SibSp         0
Parch         0
Ticket        0
Fare          1
Cabin       327
Embarked      0
dtype: int64
      train_df.isnull().sum()
      Pclass        0
Name          0
Sex           0
Age         177
SibSp         0
Parch         0
Ticket        0
Fare          0
Cabin       687
Embarked      2
dtype: int64

      decision 1 : Drop Cabin

      del train_df["Cabin"]
del test_df["Cabin"]
      all_df = train_df.append(test_df)
      all_df

      Pclass Name Sex Age SibSp Parch Ticket Fare Embarked
      PassengerId
      1 3 Braund, Mr. Owen Harris male 22.00 1 0 A/5 21171 7.25 S
      2 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.00 1 0 PC 17599 71.28 C
      3 3 Heikkinen, Miss. Laina female 26.00 0 0 STON/O2. 3101282 7.92 S
      4 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.00 1 0 113803 53.10 S
      5 3 Allen, Mr. William Henry male 35.00 0 0 373450 8.05 S
      ... ... ... ... ... ... ... ... ... ...
      1305 3 Spector, Mr. Woolf male nan 0 0 A.5. 3236 8.05 S
      1306 1 Oliva y Ocana, Dona. Fermina female 39.00 0 0 PC 17758 108.90 C
      1307 3 Saether, Mr. Simon Sivertsen male 38.50 0 0 SOTON/O.Q. 3101262 7.25 S
      1308 3 Ware, Mr. Frederick male nan 0 0 359309 8.05 S
      1309 3 Peter, Master. Michael J male nan 1 1 2668 22.36 C

      1309 rows × 9 columns

      del all_df['Name']
del all_df["Ticket"]
      all_df.head()

      Pclass Sex Age SibSp Parch Fare Embarked
      PassengerId
      1 3 male 22.00 1 0 7.25 S
      2 1 female 38.00 1 0 71.28 C
      3 3 female 26.00 0 0 7.92 S
      4 1 female 35.00 1 0 53.10 S
      5 3 male 35.00 0 0 8.05 S 
      all_df["Sex"] = all_df["Sex"].replace({"male" : 0 , "female" : 1})
all_df["Embarked"] = all_df["Embarked"].replace({"S":0,"C":1,"Q":2, np.nan:99})
      all_df.head()

      Pclass Sex Age SibSp Parch Fare Embarked
      PassengerId
      1 3 0 22.00 1 0 7.25 0
      2 1 1 38.00 1 0 71.28 1
      3 3 1 26.00 0 0 7.92 0
      4 1 1 35.00 1 0 53.10 0
      5 3 0 35.00 0 0 8.05 0 
      pd.get_dummies(all_df["Embarked"], prefix = "embarked")

      embarked_0 embarked_1 embarked_2 embarked_99
      PassengerId
      1 1 0 0 0
      2 0 1 0 0
      3 1 0 0 0
      4 1 0 0 0
      5 1 0 0 0
      ... ... ... ... ...
      1305 1 0 0 0
      1306 0 1 0 0
      1307 1 0 0 0
      1308 1 0 0 0
      1309 0 1 0 0

      1309 rows × 4 columns

      matrix_df = pd.merge(
    all_df,pd.get_dummies(all_df["Embarked"], prefix = "embarked"),
    left_index= True, right_index = True)
matrix_df.head()

      Pclass Sex Age SibSp Parch Fare Embarked embarked_0 embarked_1 embarked_2 embarked_99
      PassengerId
      1 3 0 22.00 1 0 7.25 0 1 0 0 0
      2 1 1 38.00 1 0 71.28 1 0 1 0 0
      3 3 1 26.00 0 0 7.92 0 1 0 0 0
      4 1 1 35.00 1 0 53.10 0 1 0 0 0
      5 3 0 35.00 0 0 8.05 0 1 0 0 0 
      matrix_df.corr()

      Pclass Sex Age SibSp Parch Fare Embarked embarked_0 embarked_1 embarked_2 embarked_99
      Pclass 1.00 -0.12 -0.41 0.06 0.02 -0.56 -0.05 0.10 -0.27 0.23 -0.06
      Sex -0.12 1.00 -0.06 0.11 0.21 0.19 0.07 -0.12 0.07 0.09 0.05
      Age -0.41 -0.06 1.00 -0.24 -0.15 0.18 0.07 -0.08 0.09 -0.02 0.06
      SibSp 0.06 0.11 -0.24 1.00 0.37 0.16 -0.03 0.08 -0.05 -0.05 -0.02
      Parch 0.02 0.21 -0.15 0.37 1.00 0.22 -0.03 0.07 -0.01 -0.10 -0.02
      Fare -0.56 0.19 0.18 0.16 0.22 1.00 0.05 -0.17 0.29 -0.13 0.04
      Embarked -0.05 0.07 0.07 -0.03 -0.03 0.05 1.00 -0.21 0.06 0.12 0.99
      embarked_0 0.10 -0.12 -0.08 0.08 0.07 -0.17 -0.21 1.00 -0.78 -0.49 -0.06
      embarked_1 -0.27 0.07 0.09 -0.05 -0.01 0.29 0.06 -0.78 1.00 -0.16 -0.02
      embarked_2 0.23 0.09 -0.02 -0.05 -0.10 -0.13 0.12 -0.49 -0.16 1.00 -0.01
      embarked_99 -0.06 0.05 0.06 -0.02 -0.02 0.04 0.99 -0.06 -0.02 -0.01 1.00 
      all_df.groupby("Pclass")["Age"].mean()
      Pclass
1   39.16
2   29.51
3   24.82
Name: Age, dtype: float64
      all_df.loc[(all_df["Pclass"] ==1 ) & (all_df["Age"].isnull()) , 'Age']
      PassengerId
32     nan
56     nan
65     nan
167    nan
169    nan
186    nan
257    nan
271    nan
285    nan
296    nan
299    nan
307    nan
335    nan
352    nan
376    nan
458    nan
476    nan
508    nan
528    nan
558    nan
603    nan
634    nan
670    nan
712    nan
741    nan
767    nan
794    nan
816    nan
840    nan
850    nan
914    nan
933    nan
1038   nan
1040   nan
1060   nan
1083   nan
1097   nan
1158   nan
1182   nan
Name: Age, dtype: float64
      all_df.loc[(all_df["Pclass"] == 1) & (
    all_df["Age"].isnull()), "Age"] = 39.16
all_df.loc[(all_df["Pclass"] == 2) & (all_df["Age"].isnull()) , "Age"] = 29.51
all_df.loc[(all_df["Pclass"] == 3) & (all_df["Age"].isnull()) , "Age"] = 24.82
      all_df.isnull().sum()
      Pclass      0
Sex         0
Age         0
SibSp       0
Parch       0
Fare        1
Embarked    0
dtype: int64
      all_df.groupby("Pclass")["Fare"].mean()
      Pclass
1   87.51
2   21.18
3   13.30
Name: Fare, dtype: float64
      all_df[all_df["Fare"].isnull()]

      Pclass Sex Age SibSp Parch Fare Embarked
      PassengerId
      1044 3 0 60.50 0 0 nan 0 
      all_df.loc[all_df["Fare"].isnull(), "Fare"] = 13.30
      del all_df["Embarked"]
      all_df["Pclass"] = all_df["Pclass"].replace({1:"A",2:"B",3:"C"})
      all_df = pd.get_dummies(all_df)
all_df.head()

      Sex Age SibSp Parch Fare Pclass_A Pclass_B Pclass_C
      PassengerId
      1 0 22.00 1 0 7.25 0 0 1
      2 1 38.00 1 0 71.28 1 0 0
      3 1 26.00 0 0 7.92 0 0 1
      4 1 35.00 1 0 53.10 1 0 0
      5 0 35.00 0 0 8.05 0 0 1 
      all_df = pd.merge(
    all_df, matrix_df[["embarked_0", "embarked_1" , "embarked_2","embarked_99" ]], left_index=True, right_index=True)
      train_df = all_df[all_df.index.isin(train_index)]
test_df = all_df[all_df.index.isin(test_index)]
train_df.head()

      Sex Age SibSp Parch Fare Pclass_A Pclass_B Pclass_C embarked_0 embarked_1 embarked_2 embarked_99
      PassengerId
      1 0 22.00 1 0 7.25 0 0 1 1 0 0 0
      2 1 38.00 1 0 71.28 1 0 0 0 1 0 0
      3 1 26.00 0 0 7.92 0 0 1 1 0 0 0
      4 1 35.00 1 0 53.10 1 0 0 1 0 0 0
      5 0 35.00 0 0 8.05 0 0 1 1 0 0 0

      Build Model

      x_data = train_df.values
y_data = y_train_df.values
      y_data
      array([0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1,
       1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1,
       1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1,
       1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0,
       1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0,
       0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,
       0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0,
       1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0,
       1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,
       0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0,
       0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
       1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1,
       0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1,
       1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0,
       0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0,
       0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0,
       0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1,
       0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0,
       1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0,
       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,
       1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0,
       1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0,
       0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1,
       1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
       1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0,
       0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1,
       0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0,
       0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0,
       1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1,
       0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0,
       0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0,
       1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1,
       0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0,
       0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,
       0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1,
       0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1,
       1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1,
       1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0], dtype=int64)
      from sklearn.linear_model import LogisticRegression
cls = LogisticRegression()
cls.fit(x_data,y_data)
      C:\Users\sangi\Anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(





LogisticRegression()
      cls.intercept_
      array([0.1770517])
      cls.coef_
      array([[ 2.60380921e+00, -4.11086178e-02, -3.25554029e-01,
        -8.54824009e-02,  2.34901265e-03,  1.11040984e+00,
         1.42993750e-01, -1.09047200e+00, -2.08728159e-01,
         2.07424688e-01,  9.74431422e-02,  6.67919187e-02]])
      cls.predict(test_df.values)
      array([0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0,
       1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1,
       1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1,
       1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1,
       1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,
       0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0,
       1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
       0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,
       1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
       0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,
       1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
       0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,
       0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0,
       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,
       0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,
       1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0,
       0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0,
       1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1,
       0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0],
      dtype=int64)
      x_test=test_df.values
y_test = cls.predict(x_test)
y_test
      array([0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0,
       1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1,
       1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1,
       1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1,
       1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,
       0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0,
       1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
       0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,
       1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
       0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,
       1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
       0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,
       0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0,
       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,
       0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,
       1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0,
       0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0,
       1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1,
       0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0],
      dtype=int64)
      result = np.concatenate((test_index.values.reshape(-1,1), cls.predict(x_test).reshape(-1,1)), axis =1 )
result[:5]
      array([[892,   0],
       [893,   0],
       [894,   0],
       [895,   0],
       [896,   1]], dtype=int64)
      df_submission = pd.DataFrame(result, columns = ["PassengerId", "Survived"])
      df_submission.to_csv("submission_result.csv", index=False)
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