Machine Learning on Titanic Dataset : Predicting survival
# Importing necessary libraries
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn import datasets
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as ss
sns.set(style='white')
data = pd.read_csv('Titanic_data.csv')
data
data.describe()
data.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 PassengerId 891 non-null int64
1 Survived 891 non-null int64
2 Pclass 891 non-null int64
3 Name 891 non-null object
4 Sex 891 non-null object
5 Age 714 non-null float64
6 SibSp 891 non-null int64
7 Parch 891 non-null int64
8 Ticket 891 non-null object
9 Fare 891 non-null float64
10 Cabin 204 non-null object
11 Embarked 889 non-null object
dtypes: float64(2), int64(5), object(5)
memory usage: 83.7+ KB
data.isnull().sum()
list(data.columns)
#Function to analyze and get statistical measures from numeric columns
def numerical_col(s,t):
print("++++++++++++++++++++++++++++++++++++++ "+t+" ++++++++++++++++++++++++++++++++++++++++++")
#sns.distplot(s)
sns.histplot(s)
print("Mean : ",s.mean())
print("Median : ",s.median())
print("Mode : ")
print(s.value_counts().head(1))
print("Skewness : ",ss.skew(s))
plt.axvline(s.mean(), color='r', linestyle='--')
plt.axvline(s.median(), color='g', linestyle='-')
Q1 = s.quantile(0.25)
Q3 = s.quantile(0.75)
IQR = Q3 - Q1
print("IQR : ",IQR)
print("Variance : ",s.var())
print("Standard Deviation : ",s.std())
print("Outliers : ")
l_o=0
h_o=0
for ti in s:
if ti < Q1-(1.5*IQR):
l_o+=1
elif ti > Q3+(1.5*IQR):
h_o+=1
print('Lower outlier count : ',l_o)
print('Higher outlier count : ',h_o)
print("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++")
numerical_col(data.Age,'Age')
++++++++++++++++++++++++++++++++++++++ Age ++++++++++++++++++++++++++++++++++++++++++
Mean : 29.69911764705882
Median : 28.0
Mode :
24.0 30
Name: Age, dtype: int64
Skewness : nan
IQR : 17.875
Variance : 211.01912474630802
Standard Deviation : 14.526497332334042
Outliers :
Lower outlier count : 0
Higher outlier count : 11
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
numerical_col(data.Fare,'Fare')
++++++++++++++++++++++++++++++++++++++ Fare ++++++++++++++++++++++++++++++++++++++++++
Mean : 32.204207968574636
Median : 14.4542
Mode :
8.05 43
Name: Fare, dtype: int64
Skewness : 4.7792532923723545
IQR : 23.0896
Variance : 2469.436845743116
Standard Deviation : 49.6934285971809
Outliers :
Lower outlier count : 0
Higher outlier count : 116
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
print(data['Survived'].value_counts())
sns.countplot(x='Survived',data=data)
0 549
1 342
Name: Survived, dtype: int64
print(data['Pclass'].value_counts())
sns.countplot(x='Pclass',hue='Survived',data=data)
3 491
1 216
2 184
Name: Pclass, dtype: int64
print(data['Sex'].value_counts())
sns.countplot(x='Sex',hue='Survived',data=data)
male 577
female 314
Name: Sex, dtype: int64
print(data['SibSp'].value_counts())
sns.countplot(x='SibSp',hue='Survived',data=data)
0 608
1 209
2 28
4 18
3 16
8 7
5 5
Name: SibSp, dtype: int64
print(data['Parch'].value_counts())
sns.countplot(x='Parch',hue='Survived',data=data)
0 678
1 118
2 80
3 5
5 5
4 4
6 1
Name: Parch, dtype: int64
print(data['Embarked'].value_counts())
sns.countplot(x='Embarked',hue='Survived',data=data)
S 644
C 168
Q 77
Name: Embarked, dtype: int64
data.plot.scatter(x='Age',y='Fare')
*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.
sns.scatterplot(x='Age',y='Fare',hue='Survived',data=data)
fig, axes = plt.subplots(figsize=(15, 10))
sns.heatmap(data.corr(),annot=True)
data.columns
data.drop(columns=['PassengerId','Ticket','Name','Cabin'],inplace=True) # dropping unnecessary columns
data['Age'].fillna(data['Age'].median() , inplace=True ) # replacing NA with median of age
data['Fare'].fillna(data['Fare'].median() , inplace=True ) # replacing NA with median of fare
data['Embarked'].fillna(data['Embarked'].mode()[0] , inplace=True ) # replacing NA with mode of Embarked
data.isnull().sum()
# Converting age to a categorical column
print(data.Age.min(),data.Age.max())
age_bins=[0,2,5,9,13,20,40,59,100]
age_group=['Infancy_0-2','Toddlerhood_2-5','Early_childhood_5-9',\
'Middle_Childhood_9-13','Adolescence_13-20','Early_adulthood_20-40','Middle_Adulthood_40-59','Late_Adulthood_59_100']
data['Age']=pd.cut(data['Age'],age_bins,labels=age_group)
0.42 80.0
# Converting fare to a categorical column
print(data.Fare.min(),data.Fare.max())
bins=[-1,100,250,350,550]
group=['Very_Low','Low','Medium',\
'High']
data['Fare']=pd.cut(data['Fare'],bins,labels=group)
0.0 512.3292
data.head()
data=pd.get_dummies(data,columns=[ 'Age','Fare','Pclass', 'Sex', 'SibSp', 'Parch','Embarked'])
data.head()
X=data.drop(columns=['Survived'])
y=data['Survived']
X.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 34 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Age_Infancy_0-2 891 non-null uint8
1 Age_Toddlerhood_2-5 891 non-null uint8
2 Age_Early_childhood_5-9 891 non-null uint8
3 Age_Middle_Childhood_9-13 891 non-null uint8
4 Age_Adolescence_13-20 891 non-null uint8
5 Age_Early_adulthood_20-40 891 non-null uint8
6 Age_Middle_Adulthood_40-59 891 non-null uint8
7 Age_Late_Adulthood_59_100 891 non-null uint8
8 Fare_Very_Low 891 non-null uint8
9 Fare_Low 891 non-null uint8
10 Fare_Medium 891 non-null uint8
11 Fare_High 891 non-null uint8
12 Pclass_1 891 non-null uint8
13 Pclass_2 891 non-null uint8
14 Pclass_3 891 non-null uint8
15 Sex_female 891 non-null uint8
16 Sex_male 891 non-null uint8
17 SibSp_0 891 non-null uint8
18 SibSp_1 891 non-null uint8
19 SibSp_2 891 non-null uint8
20 SibSp_3 891 non-null uint8
21 SibSp_4 891 non-null uint8
22 SibSp_5 891 non-null uint8
23 SibSp_8 891 non-null uint8
24 Parch_0 891 non-null uint8
25 Parch_1 891 non-null uint8
26 Parch_2 891 non-null uint8
27 Parch_3 891 non-null uint8
28 Parch_4 891 non-null uint8
29 Parch_5 891 non-null uint8
30 Parch_6 891 non-null uint8
31 Embarked_C 891 non-null uint8
32 Embarked_Q 891 non-null uint8
33 Embarked_S 891 non-null uint8
dtypes: uint8(34)
memory usage: 29.7 KB
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score ,confusion_matrix,classification_report
from sklearn.linear_model import LogisticRegression
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25,random_state=0)
df=pd.DataFrame(columns=['Name', 'train_accuracy', 'test_accuracy','Precision','Recall','Specificity'])
def train_test_evaluate_model(model,name):
global df
print("------------------------------------------------------------------------------------")
print(name)
model.fit(X_train,y_train)
print("Train score :")
train_score=model.score(X_train,y_train)
print(train_score)
predict= model.predict(X_test)
print("Test score :")
test_score=accuracy_score(y_test,predict)
print(test_score)
print("Confusion Matrix :")
print(confusion_matrix(y_test,predict))
print("Classification Report :")
print(classification_report(y_test,predict))
confusion__matrix = pd.crosstab(y_test,predict , rownames=['Actual'], colnames=['Predicted'])
sns.heatmap(confusion__matrix, annot=True)
plt.show()
cm=confusion_matrix(y_test,predict)
tp = cm[1,1]
tn = cm[0,0]
fp = cm[0,1]
fn = cm[1,0]
print("Accuracy : ",(tp+tn)/(tp+tn+fp+fn))
print("Precision : ",(tp)/(tp+fp))
print("Recall/TPR/Sensitivity : ",(tp)/(tp+fn))
print("Specificity/TNR : ",(tn)/(tn+fp))
print("------------------------------------------------------------------------------------")
new_row = {'Name':name, 'train_accuracy': train_score , 'test_accuracy': test_score ,'Precision': (tp)/(tp+fp) \
,'Recall': (tp)/(tp+fn) ,'Specificity': (tn)/(tn+fp) }
#append row to the dataframe
df = df.append(new_row, ignore_index=True)
train_test_evaluate_model(LogisticRegression(solver='lbfgs', max_iter=1200),"LogisticRegression")
------------------------------------------------------------------------------------
LogisticRegression
Train score :
0.8218562874251497
Test score :
0.8071748878923767
Confusion Matrix :
[[118 21]
[ 22 62]]
Classification Report :
precision recall f1-score support
0 0.84 0.85 0.85 139
1 0.75 0.74 0.74 84
accuracy 0.81 223
macro avg 0.79 0.79 0.79 223
weighted avg 0.81 0.81 0.81 223
Accuracy : 0.8071748878923767
Precision : 0.7469879518072289
Recall/TPR/Sensitivity : 0.7380952380952381
Specificity/TNR : 0.8489208633093526
------------------------------------------------------------------------------------
from sklearn.tree import DecisionTreeClassifier
train_test_evaluate_model(DecisionTreeClassifier(criterion='entropy'),"DecisionTreeClassifier")
------------------------------------------------------------------------------------
DecisionTreeClassifier
Train score :
0.875748502994012
Test score :
0.7892376681614349
Confusion Matrix :
[[123 16]
[ 31 53]]
Classification Report :
precision recall f1-score support
0 0.80 0.88 0.84 139
1 0.77 0.63 0.69 84
accuracy 0.79 223
macro avg 0.78 0.76 0.77 223
weighted avg 0.79 0.79 0.78 223
Accuracy : 0.7892376681614349
Precision : 0.7681159420289855
Recall/TPR/Sensitivity : 0.6309523809523809
Specificity/TNR : 0.8848920863309353
------------------------------------------------------------------------------------
from sklearn.ensemble import RandomForestClassifier
train_test_evaluate_model(RandomForestClassifier(n_estimators=5),"RandomForestClassifier")
------------------------------------------------------------------------------------
RandomForestClassifier
Train score :
0.8682634730538922
Test score :
0.8026905829596412
Confusion Matrix :
[[126 13]
[ 31 53]]
Classification Report :
precision recall f1-score support
0 0.80 0.91 0.85 139
1 0.80 0.63 0.71 84
accuracy 0.80 223
macro avg 0.80 0.77 0.78 223
weighted avg 0.80 0.80 0.80 223
Accuracy : 0.8026905829596412
Precision : 0.803030303030303
Recall/TPR/Sensitivity : 0.6309523809523809
Specificity/TNR : 0.9064748201438849
------------------------------------------------------------------------------------
from sklearn.neighbors import KNeighborsClassifier
train_test_evaluate_model(KNeighborsClassifier(n_neighbors=5),"KNeighborsClassifier")
------------------------------------------------------------------------------------
KNeighborsClassifier
Train score :
0.8383233532934131
Test score :
0.7892376681614349
Confusion Matrix :
[[121 18]
[ 29 55]]
Classification Report :
precision recall f1-score support
0 0.81 0.87 0.84 139
1 0.75 0.65 0.70 84
accuracy 0.79 223
macro avg 0.78 0.76 0.77 223
weighted avg 0.79 0.79 0.79 223
Accuracy : 0.7892376681614349
Precision : 0.7534246575342466
Recall/TPR/Sensitivity : 0.6547619047619048
Specificity/TNR : 0.8705035971223022
------------------------------------------------------------------------------------
from sklearn.naive_bayes import GaussianNB
train_test_evaluate_model(GaussianNB(),"GaussianNB")
------------------------------------------------------------------------------------
GaussianNB
Train score :
0.4221556886227545
Test score :
0.3991031390134529
Confusion Matrix :
[[ 7 132]
[ 2 82]]
Classification Report :
precision recall f1-score support
0 0.78 0.05 0.09 139
1 0.38 0.98 0.55 84
accuracy 0.40 223
macro avg 0.58 0.51 0.32 223
weighted avg 0.63 0.40 0.27 223
Accuracy : 0.3991031390134529
Precision : 0.38317757009345793
Recall/TPR/Sensitivity : 0.9761904761904762
Specificity/TNR : 0.050359712230215826
------------------------------------------------------------------------------------
from sklearn.svm import SVC
train_test_evaluate_model(SVC(),"SVC")
------------------------------------------------------------------------------------
SVC
Train score :
0.8368263473053892
Test score :
0.8026905829596412
Confusion Matrix :
[[121 18]
[ 26 58]]
Classification Report :
precision recall f1-score support
0 0.82 0.87 0.85 139
1 0.76 0.69 0.72 84
accuracy 0.80 223
macro avg 0.79 0.78 0.79 223
weighted avg 0.80 0.80 0.80 223
Accuracy : 0.8026905829596412
Precision : 0.7631578947368421
Recall/TPR/Sensitivity : 0.6904761904761905
Specificity/TNR : 0.8705035971223022
------------------------------------------------------------------------------------
from sklearn.ensemble import GradientBoostingClassifier
train_test_evaluate_model(GradientBoostingClassifier(),"GradientBoostingClassifier")
------------------------------------------------------------------------------------
GradientBoostingClassifier
Train score :
0.8592814371257484
Test score :
0.8071748878923767
Confusion Matrix :
[[124 15]
[ 28 56]]
Classification Report :
precision recall f1-score support
0 0.82 0.89 0.85 139
1 0.79 0.67 0.72 84
accuracy 0.81 223
macro avg 0.80 0.78 0.79 223
weighted avg 0.81 0.81 0.80 223
Accuracy : 0.8071748878923767
Precision : 0.7887323943661971
Recall/TPR/Sensitivity : 0.6666666666666666
Specificity/TNR : 0.8920863309352518
------------------------------------------------------------------------------------
!pip3 install xgboost
from xgboost import XGBClassifier
train_test_evaluate_model(XGBClassifier(),"XGBClassifier")
Collecting xgboost
Downloading xgboost-1.4.1-py3-none-manylinux2010_x86_64.whl (166.7 MB)
|████████████████████████████████| 166.7 MB 43 kB/s
Requirement already satisfied: numpy in /shared-libs/python3.7/py/lib/python3.7/site-packages (from xgboost) (1.19.5)
Requirement already satisfied: scipy in /shared-libs/python3.7/py/lib/python3.7/site-packages (from xgboost) (1.6.2)
Installing collected packages: xgboost
Successfully installed xgboost-1.4.1
WARNING: You are using pip version 21.0.1; however, version 21.1 is available.
You should consider upgrading via the '/root/venv/bin/python -m pip install --upgrade pip' command.
------------------------------------------------------------------------------------
XGBClassifier
[16:54:55] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
Train score :
0.875748502994012
Test score :
0.7668161434977578
Confusion Matrix :
[[118 21]
[ 31 53]]
Classification Report :
precision recall f1-score support
0 0.79 0.85 0.82 139
1 0.72 0.63 0.67 84
accuracy 0.77 223
macro avg 0.75 0.74 0.75 223
weighted avg 0.76 0.77 0.76 223
/root/venv/lib/python3.7/site-packages/xgboost/sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
warnings.warn(label_encoder_deprecation_msg, UserWarning)
/root/venv/lib/python3.7/site-packages/xgboost/data.py:114: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption
"because it will generate extra copies and increase " +
/root/venv/lib/python3.7/site-packages/xgboost/data.py:114: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption
"because it will generate extra copies and increase " +
Accuracy : 0.7668161434977578
Precision : 0.7162162162162162
Recall/TPR/Sensitivity : 0.6309523809523809
Specificity/TNR : 0.8489208633093526
------------------------------------------------------------------------------------
!pip3 install catboost
from catboost import CatBoostClassifier
train_test_evaluate_model(CatBoostClassifier(),"CatBoostClassifier")
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Train score :
0.8682634730538922
Test score :
0.7982062780269058
Confusion Matrix :
[[124 15]
[ 30 54]]
Classification Report :
precision recall f1-score support
0 0.81 0.89 0.85 139
1 0.78 0.64 0.71 84
accuracy 0.80 223
macro avg 0.79 0.77 0.78 223
weighted avg 0.80 0.80 0.79 223
Accuracy : 0.7982062780269058
Precision : 0.782608695652174
Recall/TPR/Sensitivity : 0.6428571428571429
Specificity/TNR : 0.8920863309352518
------------------------------------------------------------------------------------
!pip3 install lightgbm
from lightgbm import LGBMClassifier
train_test_evaluate_model(LGBMClassifier(),"LGBMClassifier")
Collecting lightgbm
Downloading lightgbm-3.2.1-py3-none-manylinux1_x86_64.whl (2.0 MB)
|████████████████████████████████| 2.0 MB 20.3 MB/s
Requirement already satisfied: wheel in /root/venv/lib/python3.7/site-packages (from lightgbm) (0.36.2)
Requirement already satisfied: numpy in /shared-libs/python3.7/py/lib/python3.7/site-packages (from lightgbm) (1.19.5)
Requirement already satisfied: scikit-learn!=0.22.0 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from lightgbm) (0.24.1)
Requirement already satisfied: scipy in /shared-libs/python3.7/py/lib/python3.7/site-packages (from lightgbm) (1.6.2)
Requirement already satisfied: joblib>=0.11 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from scikit-learn!=0.22.0->lightgbm) (1.0.1)
Requirement already satisfied: threadpoolctl>=2.0.0 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from scikit-learn!=0.22.0->lightgbm) (2.1.0)
Installing collected packages: lightgbm
Successfully installed lightgbm-3.2.1
WARNING: You are using pip version 21.0.1; however, version 21.1 is available.
You should consider upgrading via the '/root/venv/bin/python -m pip install --upgrade pip' command.
------------------------------------------------------------------------------------
LGBMClassifier
Train score :
0.8562874251497006
Test score :
0.7847533632286996
Confusion Matrix :
[[117 22]
[ 26 58]]
Classification Report :
precision recall f1-score support
0 0.82 0.84 0.83 139
1 0.72 0.69 0.71 84
accuracy 0.78 223
macro avg 0.77 0.77 0.77 223
weighted avg 0.78 0.78 0.78 223
Accuracy : 0.7847533632286996
Precision : 0.725
Recall/TPR/Sensitivity : 0.6904761904761905
Specificity/TNR : 0.841726618705036
------------------------------------------------------------------------------------
df