In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
In [2]:
df = pd.read_csv('tested_titanic.csv')
df.head()
Out[2]:
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 892 | 0 | 3 | Kelly, Mr. James | male | 34.5 | 0 | 0 | 330911 | 7.8292 | NaN | Q |
| 1 | 893 | 1 | 3 | Wilkes, Mrs. James (Ellen Needs) | female | 47.0 | 1 | 0 | 363272 | 7.0000 | NaN | S |
| 2 | 894 | 0 | 2 | Myles, Mr. Thomas Francis | male | 62.0 | 0 | 0 | 240276 | 9.6875 | NaN | Q |
| 3 | 895 | 0 | 3 | Wirz, Mr. Albert | male | 27.0 | 0 | 0 | 315154 | 8.6625 | NaN | S |
| 4 | 896 | 1 | 3 | Hirvonen, Mrs. Alexander (Helga E Lindqvist) | female | 22.0 | 1 | 1 | 3101298 | 12.2875 | NaN | S |
In [3]:
df.nunique()
Out[3]:
PassengerId 418 Survived 2 Pclass 3 Name 418 Sex 2 Age 79 SibSp 7 Parch 8 Ticket 363 Fare 169 Cabin 76 Embarked 3 dtype: int64
In [4]:
df.shape
Out[4]:
(418, 12)
In [5]:
df.describe()
Out[5]:
| PassengerId | Survived | Pclass | Age | SibSp | Parch | Fare | |
|---|---|---|---|---|---|---|---|
| count | 418.000000 | 418.000000 | 418.000000 | 332.000000 | 418.000000 | 418.000000 | 417.000000 |
| mean | 1100.500000 | 0.363636 | 2.265550 | 30.272590 | 0.447368 | 0.392344 | 35.627188 |
| std | 120.810458 | 0.481622 | 0.841838 | 14.181209 | 0.896760 | 0.981429 | 55.907576 |
| min | 892.000000 | 0.000000 | 1.000000 | 0.170000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 996.250000 | 0.000000 | 1.000000 | 21.000000 | 0.000000 | 0.000000 | 7.895800 |
| 50% | 1100.500000 | 0.000000 | 3.000000 | 27.000000 | 0.000000 | 0.000000 | 14.454200 |
| 75% | 1204.750000 | 1.000000 | 3.000000 | 39.000000 | 1.000000 | 0.000000 | 31.500000 |
| max | 1309.000000 | 1.000000 | 3.000000 | 76.000000 | 8.000000 | 9.000000 | 512.329200 |
In [6]:
df['Age'].isnull().sum()
Out[6]:
86
In [7]:
df['Survived'].value_counts()
Out[7]:
Survived 0 266 1 152 Name: count, dtype: int64
266 deaths 152 survived
In [8]:
sns.countplot(data = df, x = df['Survived'], hue = df['Pclass'])
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Cell In[8], line 1 ----> 1 sns.countplot(data = df, x = df['Survived'], hue = df['Pclass']) File ~\anaconda3\Lib\site-packages\seaborn\categorical.py:2955, in countplot(data, x, y, hue, order, hue_order, orient, color, palette, saturation, width, dodge, ax, **kwargs) 2952 if ax is None: 2953 ax = plt.gca() -> 2955 plotter.plot(ax, kwargs) 2956 return ax File ~\anaconda3\Lib\site-packages\seaborn\categorical.py:1587, in _BarPlotter.plot(self, ax, bar_kws) 1585 """Make the plot.""" 1586 self.draw_bars(ax, bar_kws) -> 1587 self.annotate_axes(ax) 1588 if self.orient == "h": 1589 ax.invert_yaxis() File ~\anaconda3\Lib\site-packages\seaborn\categorical.py:767, in _CategoricalPlotter.annotate_axes(self, ax) 764 ax.set_ylim(-.5, len(self.plot_data) - .5, auto=None) 766 if self.hue_names is not None: --> 767 ax.legend(loc="best", title=self.hue_title) File ~\anaconda3\Lib\site-packages\matplotlib\axes\_axes.py:322, in Axes.legend(self, *args, **kwargs) 204 @_docstring.dedent_interpd 205 def legend(self, *args, **kwargs): 206 """ 207 Place a legend on the Axes. 208 (...) 320 .. plot:: gallery/text_labels_and_annotations/legend.py 321 """ --> 322 handles, labels, kwargs = mlegend._parse_legend_args([self], *args, **kwargs) 323 self.legend_ = mlegend.Legend(self, handles, labels, **kwargs) 324 self.legend_._remove_method = self._remove_legend File ~\anaconda3\Lib\site-packages\matplotlib\legend.py:1361, in _parse_legend_args(axs, handles, labels, *args, **kwargs) 1357 handles = [handle for handle, label 1358 in zip(_get_legend_handles(axs, handlers), labels)] 1360 elif len(args) == 0: # 0 args: automatically detect labels and handles. -> 1361 handles, labels = _get_legend_handles_labels(axs, handlers) 1362 if not handles: 1363 log.warning( 1364 "No artists with labels found to put in legend. Note that " 1365 "artists whose label start with an underscore are ignored " 1366 "when legend() is called with no argument.") File ~\anaconda3\Lib\site-packages\matplotlib\legend.py:1291, in _get_legend_handles_labels(axs, legend_handler_map) 1289 for handle in _get_legend_handles(axs, legend_handler_map): 1290 label = handle.get_label() -> 1291 if label and not label.startswith('_'): 1292 handles.append(handle) 1293 labels.append(label) AttributeError: 'numpy.int64' object has no attribute 'startswith'
In [9]:
df['Sex']
Out[9]:
0 male
1 female
2 male
3 male
4 female
...
413 male
414 female
415 male
416 male
417 male
Name: Sex, Length: 418, dtype: object
In [10]:
sns.countplot( x = df['Sex'], hue = df['Survived'])
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Cell In[10], line 1 ----> 1 sns.countplot( x = df['Sex'], hue = df['Survived']) File ~\anaconda3\Lib\site-packages\seaborn\categorical.py:2955, in countplot(data, x, y, hue, order, hue_order, orient, color, palette, saturation, width, dodge, ax, **kwargs) 2952 if ax is None: 2953 ax = plt.gca() -> 2955 plotter.plot(ax, kwargs) 2956 return ax File ~\anaconda3\Lib\site-packages\seaborn\categorical.py:1587, in _BarPlotter.plot(self, ax, bar_kws) 1585 """Make the plot.""" 1586 self.draw_bars(ax, bar_kws) -> 1587 self.annotate_axes(ax) 1588 if self.orient == "h": 1589 ax.invert_yaxis() File ~\anaconda3\Lib\site-packages\seaborn\categorical.py:767, in _CategoricalPlotter.annotate_axes(self, ax) 764 ax.set_ylim(-.5, len(self.plot_data) - .5, auto=None) 766 if self.hue_names is not None: --> 767 ax.legend(loc="best", title=self.hue_title) File ~\anaconda3\Lib\site-packages\matplotlib\axes\_axes.py:322, in Axes.legend(self, *args, **kwargs) 204 @_docstring.dedent_interpd 205 def legend(self, *args, **kwargs): 206 """ 207 Place a legend on the Axes. 208 (...) 320 .. plot:: gallery/text_labels_and_annotations/legend.py 321 """ --> 322 handles, labels, kwargs = mlegend._parse_legend_args([self], *args, **kwargs) 323 self.legend_ = mlegend.Legend(self, handles, labels, **kwargs) 324 self.legend_._remove_method = self._remove_legend File ~\anaconda3\Lib\site-packages\matplotlib\legend.py:1361, in _parse_legend_args(axs, handles, labels, *args, **kwargs) 1357 handles = [handle for handle, label 1358 in zip(_get_legend_handles(axs, handlers), labels)] 1360 elif len(args) == 0: # 0 args: automatically detect labels and handles. -> 1361 handles, labels = _get_legend_handles_labels(axs, handlers) 1362 if not handles: 1363 log.warning( 1364 "No artists with labels found to put in legend. Note that " 1365 "artists whose label start with an underscore are ignored " 1366 "when legend() is called with no argument.") File ~\anaconda3\Lib\site-packages\matplotlib\legend.py:1291, in _get_legend_handles_labels(axs, legend_handler_map) 1289 for handle in _get_legend_handles(axs, legend_handler_map): 1290 label = handle.get_label() -> 1291 if label and not label.startswith('_'): 1292 handles.append(handle) 1293 labels.append(label) AttributeError: 'numpy.int64' object has no attribute 'startswith'
In [11]:
from sklearn.preprocessing import LabelEncoder
encoder = LabelEncoder()
df['Sex'] = encoder.fit_transform(df['Sex'])
df.head()
Out[11]:
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 892 | 0 | 3 | Kelly, Mr. James | 1 | 34.5 | 0 | 0 | 330911 | 7.8292 | NaN | Q |
| 1 | 893 | 1 | 3 | Wilkes, Mrs. James (Ellen Needs) | 0 | 47.0 | 1 | 0 | 363272 | 7.0000 | NaN | S |
| 2 | 894 | 0 | 2 | Myles, Mr. Thomas Francis | 1 | 62.0 | 0 | 0 | 240276 | 9.6875 | NaN | Q |
| 3 | 895 | 0 | 3 | Wirz, Mr. Albert | 1 | 27.0 | 0 | 0 | 315154 | 8.6625 | NaN | S |
| 4 | 896 | 1 | 3 | Hirvonen, Mrs. Alexander (Helga E Lindqvist) | 0 | 22.0 | 1 | 1 | 3101298 | 12.2875 | NaN | S |
In [12]:
sns.countplot(x = df['Sex'], hue = df['Survived'])
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Cell In[12], line 1 ----> 1 sns.countplot(x = df['Sex'], hue = df['Survived']) File ~\anaconda3\Lib\site-packages\seaborn\categorical.py:2955, in countplot(data, x, y, hue, order, hue_order, orient, color, palette, saturation, width, dodge, ax, **kwargs) 2952 if ax is None: 2953 ax = plt.gca() -> 2955 plotter.plot(ax, kwargs) 2956 return ax File ~\anaconda3\Lib\site-packages\seaborn\categorical.py:1587, in _BarPlotter.plot(self, ax, bar_kws) 1585 """Make the plot.""" 1586 self.draw_bars(ax, bar_kws) -> 1587 self.annotate_axes(ax) 1588 if self.orient == "h": 1589 ax.invert_yaxis() File ~\anaconda3\Lib\site-packages\seaborn\categorical.py:767, in _CategoricalPlotter.annotate_axes(self, ax) 764 ax.set_ylim(-.5, len(self.plot_data) - .5, auto=None) 766 if self.hue_names is not None: --> 767 ax.legend(loc="best", title=self.hue_title) File ~\anaconda3\Lib\site-packages\matplotlib\axes\_axes.py:322, in Axes.legend(self, *args, **kwargs) 204 @_docstring.dedent_interpd 205 def legend(self, *args, **kwargs): 206 """ 207 Place a legend on the Axes. 208 (...) 320 .. plot:: gallery/text_labels_and_annotations/legend.py 321 """ --> 322 handles, labels, kwargs = mlegend._parse_legend_args([self], *args, **kwargs) 323 self.legend_ = mlegend.Legend(self, handles, labels, **kwargs) 324 self.legend_._remove_method = self._remove_legend File ~\anaconda3\Lib\site-packages\matplotlib\legend.py:1361, in _parse_legend_args(axs, handles, labels, *args, **kwargs) 1357 handles = [handle for handle, label 1358 in zip(_get_legend_handles(axs, handlers), labels)] 1360 elif len(args) == 0: # 0 args: automatically detect labels and handles. -> 1361 handles, labels = _get_legend_handles_labels(axs, handlers) 1362 if not handles: 1363 log.warning( 1364 "No artists with labels found to put in legend. Note that " 1365 "artists whose label start with an underscore are ignored " 1366 "when legend() is called with no argument.") File ~\anaconda3\Lib\site-packages\matplotlib\legend.py:1291, in _get_legend_handles_labels(axs, legend_handler_map) 1289 for handle in _get_legend_handles(axs, legend_handler_map): 1290 label = handle.get_label() -> 1291 if label and not label.startswith('_'): 1292 handles.append(handle) 1293 labels.append(label) AttributeError: 'numpy.int64' object has no attribute 'startswith'
In [13]:
df['Age'].isnull().sum()
Out[13]:
86
In [14]:
sns.boxplot(df['Age'])
Out[14]:
<Axes: >
In [15]:
df['Age'].fillna(df.Age.median(), inplace = True)
In [16]:
df['Age'].isnull().sum()
Out[16]:
0
In [17]:
X = df[['Pclass', 'Sex', 'Age']]
y = df['Survived']
In [18]:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.33)
In [19]:
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression()
In [20]:
classifier.fit(X_train, y_train)
Out[20]:
LogisticRegression()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.
LogisticRegression()
In [21]:
y_pred = classifier.predict(X_test)
In [22]:
y_pred
Out[22]:
array([1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1,
0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0,
0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1,
1, 0, 0, 0, 0, 1], dtype=int64)
In [23]:
from sklearn.metrics import classification_report, accuracy_score
print("the accuracy score is ", accuracy_score(y_test, y_pred))
print(classification_report(y_test, y_pred))
the accuracy score is 1.0
precision recall f1-score support
0 1.00 1.00 1.00 93
1 1.00 1.00 1.00 45
accuracy 1.00 138
macro avg 1.00 1.00 1.00 138
weighted avg 1.00 1.00 1.00 138
In [24]:
import warnings
warnings.filterwarnings('ignore')
# female = 0 male = 1
# df[['Pclass', 'Sex', 'Age']]
# result = classifier.predict([[3,0,22]]) # survived
# result = classifier.predict([[3,1,22]]) # died
# result = classifier.predict([[2,0,22]]) # survived
# result = classifier.predict([[2,1,22]]) # died
# result = classifier.predict([[1,0,22]]) # survived
# result = classifier.predict([[1,1,22]]) # died
# result = classifier.predict([[3,0,62]]) # survived
# result = classifier.predict([[3,1,62]]) # died
# result = classifier.predict([[2,0,62]]) # died
# result = classifier.predict([[2,1,62]]) # died
# result = classifier.predict([[1,0,62]]) # died
result = classifier.predict([[1,1,62]]) # died
if(result == 0):
print("You are dead")
else:
print("Congratulations! You Survived")
You are dead
In [ ]: