In [64]:
# Setup matplotlib to plot inline (within the notebook)
%matplotlib inline
# Import the pyplot module of Matplotlib as plt
import matplotlib.pyplot as plt
# Import pandas under the abbreviation 'pd'
import pandas as pd
# Import NumPy under the abbreviation 'np'
import numpy as np
import seaborn as sns
In [65]:
titanic_train_df=pd.read_csv("train.csv")
In [66]:
titanic_train_df.head()
Out[66]:
| 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 |
In [67]:
titanic_train_df.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
In [68]:
titanic_train_df.isna().sum()
Out[68]:
PassengerId 0 Survived 0 Pclass 0 Name 0 Sex 0 Age 177 SibSp 0 Parch 0 Ticket 0 Fare 0 Cabin 687 Embarked 2 dtype: int64
In [69]:
sns.barplot(x="Sex", y="Survived", data=titanic_train_df)
plt.title("Survival Rate by Sex")
plt.show()
In [70]:
bins = [0, 12, 18, 35, 50, 80]
labels = ['0-12', '12-18', '18-35', '35-50', '50-80']
titanic_train_df['AgeGroup'] = pd.cut(
titanic_train_df['Age'],
bins=bins,
labels=labels
)
In [71]:
titanic_train_df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 891 entries, 0 to 890 Data columns (total 13 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 12 AgeGroup 714 non-null category dtypes: category(1), float64(2), int64(5), object(5) memory usage: 84.7+ KB
In [72]:
sns.countplot(data=titanic_train_df[titanic_train_df['Survived']==1],x='AgeGroup',hue='Sex')
Out[72]:
<Axes: xlabel='AgeGroup', ylabel='count'>
In [73]:
# Import Pipeline from sklearn's pipeline module
from sklearn.pipeline import Pipeline
# Import ColumnTransformer from sklearn's compose module
from sklearn.compose import ColumnTransformer
# Import SimpleImputer from sklearn's impute module
from sklearn.impute import SimpleImputer
# Import OneHotEncoder from sklearn's preprocessing module
from sklearn.preprocessing import OneHotEncoder
# Import train_test_split from sklearn's model_selection module
from sklearn.model_selection import train_test_split
In [74]:
#data processing
titanic_train_df.head()
Out[74]:
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | AgeGroup | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22.0 | 1 | 0 | A/5 21171 | 7.2500 | NaN | S | 18-35 |
| 1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | 0 | PC 17599 | 71.2833 | C85 | C | 35-50 |
| 2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26.0 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S | 18-35 |
| 3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.0 | 1 | 0 | 113803 | 53.1000 | C123 | S | 18-35 |
| 4 | 5 | 0 | 3 | Allen, Mr. William Henry | male | 35.0 | 0 | 0 | 373450 | 8.0500 | NaN | S | 18-35 |
In [75]:
titanic_train_df.columns
Out[75]:
Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',
'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked', 'AgeGroup'],
dtype='object')
In [76]:
#Feature engineering
from sklearn.preprocessing import FunctionTransformer
def extract_deck(df):
df=df.copy()
df['Deck']=df['Cabin'].str[0]
return df
def extract_title(df):
df=df.copy()
df['Title']=df['Name'].str.extract(r', ([A-Za-z]+)\.')
return df
def extract_ticket(df):
df=df.copy()
df['Ticket_prefix'] = df['Ticket'].str.extract(r'([A-Za-z\.\/]+)')
# fill missing with 'None'
df['Ticket_prefix'] = df['Ticket_prefix'].fillna('None')
return df
deck_extract_transormer=FunctionTransformer(extract_deck)
title_extract_transformer=FunctionTransformer(extract_title)
ticket_extract_transformer=FunctionTransformer(extract_ticket)
In [77]:
# creating tranformers
name_feature=['Name']
name_transformer=Pipeline(steps=[('extract_title',title_extract_transformer),
('imputer',SimpleImputer(strategy='constant',fill_value='missing')),
('onehot',OneHotEncoder(handle_unknown='ignore'))])
ticket_feature=['Ticket']
ticket_transformer=Pipeline(steps=[('extract_ticket',ticket_extract_transformer),
('imputer',SimpleImputer(strategy='constant',fill_value='missing')),
('onehot',OneHotEncoder(handle_unknown='ignore'))])
cabin_feature=['Cabin']
cabin_transformer=Pipeline(steps=[('extract_deck',deck_extract_transormer),
('imputer',SimpleImputer(strategy='constant',fill_value='missing')),
('onehot',OneHotEncoder(handle_unknown='ignore'))])
categorical_feature=['Sex','Embarked']
categorical_transformer=Pipeline(steps=[('imputer',SimpleImputer(strategy='most_frequent')),
('onehot',OneHotEncoder(handle_unknown='ignore'))])
numeric_features=['Age']
numeric_transformer=Pipeline(steps=[('imputer',SimpleImputer(strategy='median'))])
In [78]:
preprocessor=ColumnTransformer(transformers=[('name',name_transformer,name_feature),
('ticket',ticket_transformer,ticket_feature),
('cabin',cabin_transformer,cabin_feature),
('cat',categorical_transformer,categorical_feature),
('num',numeric_transformer,numeric_features)])
In [79]:
X=titanic_train_df[['Pclass','Name','Sex','Age','SibSp','Parch','Ticket','Fare','Cabin','Embarked']]
y=titanic_train_df['Survived']
In [80]:
titanic_X_train, titanic_X_test, titanic_y_train, titanic_y_test=train_test_split(X,y,test_size=0.2,random_state=42)
In [81]:
titanic_X_train.shape, titanic_X_test.shape, titanic_y_train.shape, titanic_y_test.shape
Out[81]:
((712, 10), (179, 10), (712,), (179,))
In [82]:
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from xgboost import XGBClassifier
In [83]:
classification_models = {"LogisticRegression": LogisticRegression(max_iter=1000, solver="lbfgs", random_state=42),
"RandomForestClassifier": RandomForestClassifier(n_estimators=200, random_state=42, n_jobs=-1),
"XGBClassifier": XGBClassifier(n_estimators=300, learning_rate=0.05, max_depth=4,
subsample=0.8, colsample_bytree=0.8,
random_state=42, use_label_encoder=False, eval_metric="logloss")}
# Create an empty dictionary for the regression results
classification_results = {}
In [84]:
for model_name, model in classification_models.items():
model_pipeline=Pipeline(steps=[('preprocessor',preprocessor),
('model',model)])
print(f'Fitting {model_name}...')
model_pipeline.fit(titanic_X_train,titanic_y_train)
print(f'Scoring {model_name}...')
classification_results[model_name]=model_pipeline.score(titanic_X_test,titanic_y_test)
Fitting LogisticRegression... Scoring LogisticRegression... Fitting RandomForestClassifier... Scoring RandomForestClassifier... Fitting XGBClassifier...
c:\Users\RaafatZiade\Desktop\The AI Engineer\Titanic\myenv\Lib\site-packages\xgboost\training.py:183: UserWarning: [23:44:05] WARNING: C:\actions-runner\_work\xgboost\xgboost\src\learner.cc:738:
Parameters: { "use_label_encoder" } are not used.
bst.update(dtrain, iteration=i, fobj=obj)
Scoring XGBClassifier...
In [85]:
classification_results
Out[85]:
{'LogisticRegression': 0.8324022346368715,
'RandomForestClassifier': 0.8268156424581006,
'XGBClassifier': 0.7988826815642458}
In [88]:
#Hypertuning best model
np.random.seed(42)
from sklearn.model_selection import RandomizedSearchCV
from scipy.stats import uniform
param_distributions = {
'model__C': uniform(0.01, 10), # C between 0.01 and 10
'model__penalty': ['l1', 'l2', 'elasticnet'],
'model__solver': ['liblinear', 'saga'],
'model__max_iter': [100, 500, 1000],
}
In [90]:
logsiticRegression_pipeline=Pipeline(steps=[('preprocessor',preprocessor),
('model',LogisticRegression())])
randomized_search_lr=RandomizedSearchCV(estimator=logsiticRegression_pipeline,
param_distributions=param_distributions,
cv=5,
n_iter=5,
verbose=True)
randomized_search_lr.fit(titanic_X_train,titanic_y_train)
Fitting 5 folds for each of 5 candidates, totalling 25 fits
c:\Users\RaafatZiade\Desktop\The AI Engineer\Titanic\myenv\Lib\site-packages\sklearn\linear_model\_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
c:\Users\RaafatZiade\Desktop\The AI Engineer\Titanic\myenv\Lib\site-packages\sklearn\linear_model\_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
c:\Users\RaafatZiade\Desktop\The AI Engineer\Titanic\myenv\Lib\site-packages\sklearn\linear_model\_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
c:\Users\RaafatZiade\Desktop\The AI Engineer\Titanic\myenv\Lib\site-packages\sklearn\linear_model\_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
c:\Users\RaafatZiade\Desktop\The AI Engineer\Titanic\myenv\Lib\site-packages\sklearn\linear_model\_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
c:\Users\RaafatZiade\Desktop\The AI Engineer\Titanic\myenv\Lib\site-packages\sklearn\linear_model\_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
c:\Users\RaafatZiade\Desktop\The AI Engineer\Titanic\myenv\Lib\site-packages\sklearn\linear_model\_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
c:\Users\RaafatZiade\Desktop\The AI Engineer\Titanic\myenv\Lib\site-packages\sklearn\linear_model\_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
c:\Users\RaafatZiade\Desktop\The AI Engineer\Titanic\myenv\Lib\site-packages\sklearn\linear_model\_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
c:\Users\RaafatZiade\Desktop\The AI Engineer\Titanic\myenv\Lib\site-packages\sklearn\linear_model\_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
c:\Users\RaafatZiade\Desktop\The AI Engineer\Titanic\myenv\Lib\site-packages\sklearn\linear_model\_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
c:\Users\RaafatZiade\Desktop\The AI Engineer\Titanic\myenv\Lib\site-packages\sklearn\linear_model\_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
c:\Users\RaafatZiade\Desktop\The AI Engineer\Titanic\myenv\Lib\site-packages\sklearn\linear_model\_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
c:\Users\RaafatZiade\Desktop\The AI Engineer\Titanic\myenv\Lib\site-packages\sklearn\linear_model\_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
c:\Users\RaafatZiade\Desktop\The AI Engineer\Titanic\myenv\Lib\site-packages\sklearn\linear_model\_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
warnings.warn(
c:\Users\RaafatZiade\Desktop\The AI Engineer\Titanic\myenv\Lib\site-packages\sklearn\model_selection\_validation.py:516: FitFailedWarning:
5 fits failed out of a total of 25.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.
Below are more details about the failures:
--------------------------------------------------------------------------------
5 fits failed with the following error:
Traceback (most recent call last):
File "c:\Users\RaafatZiade\Desktop\The AI Engineer\Titanic\myenv\Lib\site-packages\sklearn\model_selection\_validation.py", line 859, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "c:\Users\RaafatZiade\Desktop\The AI Engineer\Titanic\myenv\Lib\site-packages\sklearn\base.py", line 1365, in wrapper
return fit_method(estimator, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "c:\Users\RaafatZiade\Desktop\The AI Engineer\Titanic\myenv\Lib\site-packages\sklearn\pipeline.py", line 663, in fit
self._final_estimator.fit(Xt, y, **last_step_params["fit"])
File "c:\Users\RaafatZiade\Desktop\The AI Engineer\Titanic\myenv\Lib\site-packages\sklearn\base.py", line 1365, in wrapper
return fit_method(estimator, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "c:\Users\RaafatZiade\Desktop\The AI Engineer\Titanic\myenv\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1228, in fit
raise ValueError("l1_ratio must be specified when penalty is elasticnet.")
ValueError: l1_ratio must be specified when penalty is elasticnet.
warnings.warn(some_fits_failed_message, FitFailedWarning)
c:\Users\RaafatZiade\Desktop\The AI Engineer\Titanic\myenv\Lib\site-packages\sklearn\model_selection\_search.py:1135: UserWarning: One or more of the test scores are non-finite: [0.79069241 0.83568403 0.80894317 0.80894317 nan]
warnings.warn(
Out[90]:
RandomizedSearchCV(cv=5,
estimator=Pipeline(steps=[('preprocessor',
ColumnTransformer(transformers=[('name',
Pipeline(steps=[('extract_title',
FunctionTransformer(func=<function extract_title at 0x00000232FB7DE480>)),
('imputer',
SimpleImputer(fill_value='missing',
strategy='constant')),
('onehot',
OneHotEncoder(handle_unknown='ignore'))]),
['Name']),
('ticket',...
Pipeline(steps=[('imputer',
SimpleImputer(strategy='median'))]),
['Age'])])),
('model', LogisticRegression())]),
n_iter=5,
param_distributions={'model__C': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x00000232FEA43510>,
'model__max_iter': [100, 500, 1000],
'model__penalty': ['l1', 'l2',
'elasticnet'],
'model__solver': ['liblinear', 'saga']},
verbose=True)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Parameters
| estimator | Pipeline(step...egression())]) | |
| param_distributions | {'model__C': <scipy.stats....00232FEA43510>, 'model__max_iter': [100, 500, ...], 'model__penalty': ['l1', 'l2', ...], 'model__solver': ['liblinear', 'saga']} | |
| n_iter | 5 | |
| scoring | None | |
| n_jobs | None | |
| refit | True | |
| cv | 5 | |
| verbose | True | |
| pre_dispatch | '2*n_jobs' | |
| random_state | None | |
| error_score | nan | |
| return_train_score | False |
Parameters
| transformers | [('name', ...), ('ticket', ...), ...] | |
| remainder | 'drop' | |
| sparse_threshold | 0.3 | |
| n_jobs | None | |
| transformer_weights | None | |
| verbose | False | |
| verbose_feature_names_out | True | |
| force_int_remainder_cols | 'deprecated' |
['Name']
Parameters
| func | <function ext...00232FB7DE480> | |
| inverse_func | None | |
| validate | False | |
| accept_sparse | False | |
| check_inverse | True | |
| feature_names_out | None | |
| kw_args | None | |
| inv_kw_args | None |
Parameters
| missing_values | nan | |
| strategy | 'constant' | |
| fill_value | 'missing' | |
| copy | True | |
| add_indicator | False | |
| keep_empty_features | False |
Parameters
| categories | 'auto' | |
| drop | None | |
| sparse_output | True | |
| dtype | <class 'numpy.float64'> | |
| handle_unknown | 'ignore' | |
| min_frequency | None | |
| max_categories | None | |
| feature_name_combiner | 'concat' |
['Ticket']
Parameters
| func | <function ext...00232FB7DE520> | |
| inverse_func | None | |
| validate | False | |
| accept_sparse | False | |
| check_inverse | True | |
| feature_names_out | None | |
| kw_args | None | |
| inv_kw_args | None |
Parameters
| missing_values | nan | |
| strategy | 'constant' | |
| fill_value | 'missing' | |
| copy | True | |
| add_indicator | False | |
| keep_empty_features | False |
Parameters
| categories | 'auto' | |
| drop | None | |
| sparse_output | True | |
| dtype | <class 'numpy.float64'> | |
| handle_unknown | 'ignore' | |
| min_frequency | None | |
| max_categories | None | |
| feature_name_combiner | 'concat' |
['Cabin']
Parameters
| func | <function ext...00232FB7DE840> | |
| inverse_func | None | |
| validate | False | |
| accept_sparse | False | |
| check_inverse | True | |
| feature_names_out | None | |
| kw_args | None | |
| inv_kw_args | None |
Parameters
| missing_values | nan | |
| strategy | 'constant' | |
| fill_value | 'missing' | |
| copy | True | |
| add_indicator | False | |
| keep_empty_features | False |
Parameters
| categories | 'auto' | |
| drop | None | |
| sparse_output | True | |
| dtype | <class 'numpy.float64'> | |
| handle_unknown | 'ignore' | |
| min_frequency | None | |
| max_categories | None | |
| feature_name_combiner | 'concat' |
['Sex', 'Embarked']
Parameters
| missing_values | nan | |
| strategy | 'most_frequent' | |
| fill_value | None | |
| copy | True | |
| add_indicator | False | |
| keep_empty_features | False |
Parameters
| categories | 'auto' | |
| drop | None | |
| sparse_output | True | |
| dtype | <class 'numpy.float64'> | |
| handle_unknown | 'ignore' | |
| min_frequency | None | |
| max_categories | None | |
| feature_name_combiner | 'concat' |
['Age']
Parameters
| missing_values | nan | |
| strategy | 'median' | |
| fill_value | None | |
| copy | True | |
| add_indicator | False | |
| keep_empty_features | False |
Parameters
| penalty | 'l1' | |
| dual | False | |
| tol | 0.0001 | |
| C | np.float64(6.126531604882809) | |
| fit_intercept | True | |
| intercept_scaling | 1 | |
| class_weight | None | |
| random_state | None | |
| solver | 'liblinear' | |
| max_iter | 100 | |
| multi_class | 'deprecated' | |
| verbose | 0 | |
| warm_start | False | |
| n_jobs | None | |
| l1_ratio | None |
In [91]:
randomized_search_lr.best_params_
Out[91]:
{'model__C': np.float64(6.126531604882809),
'model__max_iter': 100,
'model__penalty': 'l1',
'model__solver': 'liblinear'}
In [107]:
# Import confusion_matrix and classification_report from sklearn's metrics module
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
# Import precision_score, recall_score and f1_score from sklearn's metrics module
from sklearn.metrics import precision_score, recall_score, f1_score
# Import plot_roc_curve from sklearn's metrics module
from sklearn.metrics import RocCurveDisplay
import matplotlib.pyplot as plt
best_logreg =Pipeline(steps=[('preprocessor',preprocessor),('best logR', LogisticRegression(
C=6.126531604882809,
max_iter=100,
penalty='l1',
solver='liblinear'))])
In [131]:
best_logreg.fit(titanic_X_train,titanic_y_train)
Out[131]:
Pipeline(steps=[('preprocessor',
ColumnTransformer(transformers=[('name',
Pipeline(steps=[('extract_title',
FunctionTransformer(func=<function extract_title at 0x00000232FB7DE480>)),
('imputer',
SimpleImputer(fill_value='missing',
strategy='constant')),
('onehot',
OneHotEncoder(handle_unknown='ignore'))]),
['Name']),
('ticket',
Pipeline(steps=[('extract_ticket',...
OneHotEncoder(handle_unknown='ignore'))]),
['Cabin']),
('cat',
Pipeline(steps=[('imputer',
SimpleImputer(strategy='most_frequent')),
('onehot',
OneHotEncoder(handle_unknown='ignore'))]),
['Sex', 'Embarked']),
('num',
Pipeline(steps=[('imputer',
SimpleImputer(strategy='median'))]),
['Age'])])),
('best logR',
LogisticRegression(C=6.126531604882809, penalty='l1',
solver='liblinear'))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Parameters
| steps | [('preprocessor', ...), ('best logR', ...)] | |
| transform_input | None | |
| memory | None | |
| verbose | False |
Parameters
| transformers | [('name', ...), ('ticket', ...), ...] | |
| remainder | 'drop' | |
| sparse_threshold | 0.3 | |
| n_jobs | None | |
| transformer_weights | None | |
| verbose | False | |
| verbose_feature_names_out | True | |
| force_int_remainder_cols | 'deprecated' |
['Name']
Parameters
| func | <function ext...00232FB7DE480> | |
| inverse_func | None | |
| validate | False | |
| accept_sparse | False | |
| check_inverse | True | |
| feature_names_out | None | |
| kw_args | None | |
| inv_kw_args | None |
Parameters
| missing_values | nan | |
| strategy | 'constant' | |
| fill_value | 'missing' | |
| copy | True | |
| add_indicator | False | |
| keep_empty_features | False |
Parameters
| categories | 'auto' | |
| drop | None | |
| sparse_output | True | |
| dtype | <class 'numpy.float64'> | |
| handle_unknown | 'ignore' | |
| min_frequency | None | |
| max_categories | None | |
| feature_name_combiner | 'concat' |
['Ticket']
Parameters
| func | <function ext...00232FB7DE520> | |
| inverse_func | None | |
| validate | False | |
| accept_sparse | False | |
| check_inverse | True | |
| feature_names_out | None | |
| kw_args | None | |
| inv_kw_args | None |
Parameters
| missing_values | nan | |
| strategy | 'constant' | |
| fill_value | 'missing' | |
| copy | True | |
| add_indicator | False | |
| keep_empty_features | False |
Parameters
| categories | 'auto' | |
| drop | None | |
| sparse_output | True | |
| dtype | <class 'numpy.float64'> | |
| handle_unknown | 'ignore' | |
| min_frequency | None | |
| max_categories | None | |
| feature_name_combiner | 'concat' |
['Cabin']
Parameters
| func | <function ext...00232FB7DE840> | |
| inverse_func | None | |
| validate | False | |
| accept_sparse | False | |
| check_inverse | True | |
| feature_names_out | None | |
| kw_args | None | |
| inv_kw_args | None |
Parameters
| missing_values | nan | |
| strategy | 'constant' | |
| fill_value | 'missing' | |
| copy | True | |
| add_indicator | False | |
| keep_empty_features | False |
Parameters
| categories | 'auto' | |
| drop | None | |
| sparse_output | True | |
| dtype | <class 'numpy.float64'> | |
| handle_unknown | 'ignore' | |
| min_frequency | None | |
| max_categories | None | |
| feature_name_combiner | 'concat' |
['Sex', 'Embarked']
Parameters
| missing_values | nan | |
| strategy | 'most_frequent' | |
| fill_value | None | |
| copy | True | |
| add_indicator | False | |
| keep_empty_features | False |
Parameters
| categories | 'auto' | |
| drop | None | |
| sparse_output | True | |
| dtype | <class 'numpy.float64'> | |
| handle_unknown | 'ignore' | |
| min_frequency | None | |
| max_categories | None | |
| feature_name_combiner | 'concat' |
['Age']
Parameters
| missing_values | nan | |
| strategy | 'median' | |
| fill_value | None | |
| copy | True | |
| add_indicator | False | |
| keep_empty_features | False |
Parameters
| penalty | 'l1' | |
| dual | False | |
| tol | 0.0001 | |
| C | 6.126531604882809 | |
| fit_intercept | True | |
| intercept_scaling | 1 | |
| class_weight | None | |
| random_state | None | |
| solver | 'liblinear' | |
| max_iter | 100 | |
| multi_class | 'deprecated' | |
| verbose | 0 | |
| warm_start | False | |
| n_jobs | None | |
| l1_ratio | None |
In [101]:
y_predict=best_logreg.predict(titanic_X_test)
In [105]:
a=confusion_matrix(titanic_y_test,y_predict)
In [106]:
from sklearn.metrics import ConfusionMatrixDisplay
fig, ax = plt.subplots(figsize=(3,3))
ConfusionMatrixDisplay(a).plot(cmap="Blues", colorbar=False, values_format="d", ax=ax)
ax.set_xlabel("Predicted label"); ax.set_ylabel("True label")
plt.tight_layout(); plt.show()
In [108]:
print(classification_report(titanic_y_test,y_predict))
precision recall f1-score support
0 0.85 0.89 0.87 105
1 0.83 0.78 0.81 74
accuracy 0.84 179
macro avg 0.84 0.83 0.84 179
weighted avg 0.84 0.84 0.84 179
In [109]:
#cross-validation evaluation
from sklearn.model_selection import cross_val_score
In [110]:
cross_val_score(best_logreg,
X,
y,
scoring="accuracy",
cv=5)
Out[110]:
array([0.86592179, 0.8258427 , 0.83146067, 0.81460674, 0.88202247])
In [112]:
cross_val_acc = np.mean(cross_val_score(best_logreg,
X,
y,
scoring="accuracy",
cv=5))
cross_val_acc
Out[112]:
np.float64(0.8417299604544599)
In [114]:
cross_val_precision = np.mean(cross_val_score(best_logreg,
X,
y,
scoring="precision",
cv=5))
cross_val_precision
Out[114]:
np.float64(0.8277266150952979)
In [115]:
cross_val_recall = np.mean(cross_val_score(best_logreg,
X,
y,
scoring="recall",
cv=5))
cross_val_recall
Out[115]:
np.float64(0.7598039215686275)
In [116]:
cross_val_f1 = np.mean(cross_val_score(best_logreg,
X,
y,
scoring="f1",
cv=5))
cross_val_f1
Out[116]:
np.float64(0.7880810049602147)
In [117]:
#Predit on real values from test.csv
test_df=pd.read_csv('test.csv')
In [118]:
test_df.head()
Out[118]:
| PassengerId | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 892 | 3 | Kelly, Mr. James | male | 34.5 | 0 | 0 | 330911 | 7.8292 | NaN | Q |
| 1 | 893 | 3 | Wilkes, Mrs. James (Ellen Needs) | female | 47.0 | 1 | 0 | 363272 | 7.0000 | NaN | S |
| 2 | 894 | 2 | Myles, Mr. Thomas Francis | male | 62.0 | 0 | 0 | 240276 | 9.6875 | NaN | Q |
| 3 | 895 | 3 | Wirz, Mr. Albert | male | 27.0 | 0 | 0 | 315154 | 8.6625 | NaN | S |
| 4 | 896 | 3 | Hirvonen, Mrs. Alexander (Helga E Lindqvist) | female | 22.0 | 1 | 1 | 3101298 | 12.2875 | NaN | S |
In [120]:
test_df.isna().sum()
Out[120]:
PassengerId 0 Pclass 0 Name 0 Sex 0 Age 86 SibSp 0 Parch 0 Ticket 0 Fare 1 Cabin 327 Embarked 0 dtype: int64
In [127]:
x_prime=test_df[['Pclass','Name','Sex','Age','SibSp','Parch','Ticket','Fare','Cabin','Embarked']]
#one fare value missing, lets drop it!
x_prime=x_prime.dropna(subset='Fare',axis=0)
In [129]:
# making sure all is set before predicting
x_prime.isna().sum()
Out[129]:
Pclass 0 Name 0 Sex 0 Age 86 SibSp 0 Parch 0 Ticket 0 Fare 0 Cabin 326 Embarked 0 dtype: int64
In [133]:
#great lets predict
predictions=best_logreg.predict(x_prime)
In [138]:
x_fixed=test_df.dropna(subset='Fare',axis=0)
submission = pd.DataFrame({
"PassengerId":x_fixed["PassengerId"],
"Survived": predictions})
In [139]:
submission.to_csv("titanic_prediction_submission.csv", index=False)
In [140]:
submission.head()
Out[140]:
| PassengerId | Survived | |
|---|---|---|
| 0 | 892 | 0 |
| 1 | 893 | 1 |
| 2 | 894 | 0 |
| 3 | 895 | 0 |
| 4 | 896 | 1 |
In [ ]: