184 KiB
184 KiB
Titanic Machine Learning from Disaster
Imports
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as pxData description
# Loading the data
df_train = pd.read_csv('train.csv')
df_test = pd.read_csv('test.csv')df_train.columnsIndex(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',
'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],
dtype='object')df_test.columnsIndex(['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch',
'Ticket', 'Fare', 'Cabin', 'Embarked'],
dtype='object')df_train.describe()| PassengerId | Survived | Pclass | Age | SibSp | Parch | Fare | |
|---|---|---|---|---|---|---|---|
| count | 891.000000 | 891.000000 | 891.000000 | 714.000000 | 891.000000 | 891.000000 | 891.000000 |
| mean | 446.000000 | 0.383838 | 2.308642 | 29.699118 | 0.523008 | 0.381594 | 32.204208 |
| std | 257.353842 | 0.486592 | 0.836071 | 14.526497 | 1.102743 | 0.806057 | 49.693429 |
| min | 1.000000 | 0.000000 | 1.000000 | 0.420000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 223.500000 | 0.000000 | 2.000000 | 20.125000 | 0.000000 | 0.000000 | 7.910400 |
| 50% | 446.000000 | 0.000000 | 3.000000 | 28.000000 | 0.000000 | 0.000000 | 14.454200 |
| 75% | 668.500000 | 1.000000 | 3.000000 | 38.000000 | 1.000000 | 0.000000 | 31.000000 |
| max | 891.000000 | 1.000000 | 3.000000 | 80.000000 | 8.000000 | 6.000000 | 512.329200 |
df_test.describe()| PassengerId | Pclass | Age | SibSp | Parch | Fare | |
|---|---|---|---|---|---|---|
| count | 418.000000 | 418.000000 | 332.000000 | 418.000000 | 418.000000 | 417.000000 |
| mean | 1100.500000 | 2.265550 | 30.272590 | 0.447368 | 0.392344 | 35.627188 |
| std | 120.810458 | 0.841838 | 14.181209 | 0.896760 | 0.981429 | 55.907576 |
| min | 892.000000 | 1.000000 | 0.170000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 996.250000 | 1.000000 | 21.000000 | 0.000000 | 0.000000 | 7.895800 |
| 50% | 1100.500000 | 3.000000 | 27.000000 | 0.000000 | 0.000000 | 14.454200 |
| 75% | 1204.750000 | 3.000000 | 39.000000 | 1.000000 | 0.000000 | 31.500000 |
| max | 1309.000000 | 3.000000 | 76.000000 | 8.000000 | 9.000000 | 512.329200 |
df_train.isna().sum()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
df_test.isna().sum()PassengerId 0 Pclass 0 Name 0 Sex 0 Age 86 SibSp 0 Parch 0 Ticket 0 Fare 1 Cabin 327 Embarked 0 dtype: int64
df_test['Fare'].fillna(df_test['Fare'].mean(), inplace=True)df_test['Cabin'].fillna('Other', inplace=True)Preexploratory Data Analysis
len(df_train[df_train['Survived']==1]), len(df_train[df_train['Survived']==0])(342, 549)
sns.catplot(x='Sex', y='Survived', data=df_train, kind='violin')<seaborn.axisgrid.FacetGrid at 0x1e194179a00>
sns.barplot(x='Sex', y='Survived', data=df_train)<AxesSubplot:xlabel='Sex', ylabel='Survived'>
sns.histplot(x='Age', hue='Survived', data=df_train, bins=20)<AxesSubplot:xlabel='Age', ylabel='Count'>
sns.histplot(x='Pclass', hue='Survived', data=df_train, bins=20)<AxesSubplot:xlabel='Pclass', ylabel='Count'>
# sns.pairplot(data=df_train, hue='Survived')Data Cleaning
df_train = pd.get_dummies(data=df_train, columns=['Sex', 'Embarked'])
df_test = pd.get_dummies(data=df_test, columns=['Sex', 'Embarked'])# df_train.drop(['Sex_male', 'Name', 'Ticket', 'PassengerId'], axis=1, inplace=True)
df_train.drop('Sex_male', axis=1, inplace=True)
df_test.drop('Sex_male', axis=1, inplace=True)df_train['Age'] = df_train['Age'].fillna(df_train['Age'].mean())
df_test['Age'] = df_test['Age'].fillna(df_train['Age'].mean())df_train['Cabin'] = df_train['Cabin'].fillna('Other')df_train['Cabin'].unique()array(['Other', 'C85', 'C123', 'E46', 'G6', 'C103', 'D56', 'A6',
'C23 C25 C27', 'B78', 'D33', 'B30', 'C52', 'B28', 'C83', 'F33',
'F G73', 'E31', 'A5', 'D10 D12', 'D26', 'C110', 'B58 B60', 'E101',
'F E69', 'D47', 'B86', 'F2', 'C2', 'E33', 'B19', 'A7', 'C49', 'F4',
'A32', 'B4', 'B80', 'A31', 'D36', 'D15', 'C93', 'C78', 'D35',
'C87', 'B77', 'E67', 'B94', 'C125', 'C99', 'C118', 'D7', 'A19',
'B49', 'D', 'C22 C26', 'C106', 'C65', 'E36', 'C54',
'B57 B59 B63 B66', 'C7', 'E34', 'C32', 'B18', 'C124', 'C91', 'E40',
'T', 'C128', 'D37', 'B35', 'E50', 'C82', 'B96 B98', 'E10', 'E44',
'A34', 'C104', 'C111', 'C92', 'E38', 'D21', 'E12', 'E63', 'A14',
'B37', 'C30', 'D20', 'B79', 'E25', 'D46', 'B73', 'C95', 'B38',
'B39', 'B22', 'C86', 'C70', 'A16', 'C101', 'C68', 'A10', 'E68',
'B41', 'A20', 'D19', 'D50', 'D9', 'A23', 'B50', 'A26', 'D48',
'E58', 'C126', 'B71', 'B51 B53 B55', 'D49', 'B5', 'B20', 'F G63',
'C62 C64', 'E24', 'C90', 'C45', 'E8', 'B101', 'D45', 'C46', 'D30',
'E121', 'D11', 'E77', 'F38', 'B3', 'D6', 'B82 B84', 'D17', 'A36',
'B102', 'B69', 'E49', 'C47', 'D28', 'E17', 'A24', 'C50', 'B42',
'C148'], dtype=object)df_train['Cabin'].value_counts()Other 687
C23 C25 C27 4
G6 4
B96 B98 4
C22 C26 3
...
E34 1
C7 1
C54 1
E36 1
C148 1
Name: Cabin, Length: 148, dtype: int64# df_train['Cabin'].str.extract('(\d+)')df_train['Cabin symbol'] = df_train['Cabin'].str.extract('(\w)')
df_test['Cabin symbol'] = df_test['Cabin'].str.extract('(\w)')df_test['Cabin'].str.extract('(\w)').value_counts()O 327 C 35 B 18 D 13 E 9 F 8 A 7 G 1 dtype: int64
symbol_hist = df_train[df_train['Cabin symbol'] != 'O'][['Cabin symbol', 'Survived']]sns.histplot(x='Cabin symbol', hue='Survived', data=symbol_hist, bins=20)<AxesSubplot:xlabel='Cabin symbol', ylabel='Count'>
# Describe the 'Cabin' with number of people in it
counts_train = df_train['Cabin'].value_counts().copy(deep=True)
counts_test = df_test['Cabin'].value_counts().copy(deep=True)
# Changing n-people cabin to 'description'
def num_peopl_in_cabin(df, n, description, counts):
df['Cabin'][df['Cabin'].isin(counts[counts==n].index)] = description
num_peopl_in_cabin(df_train, 1, 'Alone', counts_train)
num_peopl_in_cabin(df_train, 2, 'Double room', counts_train)
num_peopl_in_cabin(df_train, 3, 'Three person room', counts_train)
num_peopl_in_cabin(df_train, 4, 'Four person room', counts_train)
num_peopl_in_cabin(df_test, 1, 'Alone', counts_test)
num_peopl_in_cabin(df_test, 2, 'Double room', counts_test)
num_peopl_in_cabin(df_test, 3, 'Three person room', counts_test)
# df_train['Cabin'][df_train['Cabin'].isin(counts[counts>4].index)] = 'Other'
# df_train['Cabin'][df_train['Cabin'].isin(counts[counts==1].index)] = 'Alone'
# df_train['Cabin'][df_train['Cabin'].isin(counts[counts==2].index)] = 'Double room'
# df_train['Cabin'][df_train['Cabin'].isin(counts[counts==3].index)] = 'Three person room'
# df_train['Cabin'][df_train['Cabin'].isin(counts[counts==4].index)] = 'Four person room'
# df_train['Cabin'][df_train['Cabin'].isin(counts[counts>4].index)] = 'Other'C:\Users\Maciej\AppData\Local\Temp/ipykernel_16012/2825624458.py:7: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df['Cabin'][df['Cabin'].isin(counts[counts==n].index)] = description
df_train['Cabin'].value_counts()Other 687 Alone 101 Double room 76 Three person room 15 Four person room 12 Name: Cabin, dtype: int64
df_train = pd.get_dummies(data=df_train, columns=['Cabin'])
df_test = pd.get_dummies(data=df_test, columns=['Cabin', 'Pclass', 'Cabin symbol'])df_train = pd.get_dummies(data=df_train, columns=['Pclass'])df_train = pd.get_dummies(data=df_train, columns=['Cabin symbol'])df_train = df_train.drop('Cabin symbol_O', axis=1)
df_test = df_test.drop('Cabin symbol_O', axis=1)df_test.columnsIndex(['PassengerId', 'Name', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare',
'Sex_female', 'Embarked_C', 'Embarked_Q', 'Embarked_S', 'Cabin_Alone',
'Cabin_Double room', 'Cabin_Other', 'Cabin_Three person room',
'Pclass_1', 'Pclass_2', 'Pclass_3', 'Cabin symbol_A', 'Cabin symbol_B',
'Cabin symbol_C', 'Cabin symbol_D', 'Cabin symbol_E', 'Cabin symbol_F',
'Cabin symbol_G'],
dtype='object')df_test.columns[df_test.columns.isin(df_train.columns)]Index(['PassengerId', 'Name', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare',
'Sex_female', 'Embarked_C', 'Embarked_Q', 'Embarked_S', 'Cabin_Alone',
'Cabin_Double room', 'Cabin_Other', 'Cabin_Three person room',
'Pclass_1', 'Pclass_2', 'Pclass_3', 'Cabin symbol_A', 'Cabin symbol_B',
'Cabin symbol_C', 'Cabin symbol_D', 'Cabin symbol_E', 'Cabin symbol_F',
'Cabin symbol_G'],
dtype='object')df_train.columnsIndex(['PassengerId', 'Survived', 'Name', 'Age', 'SibSp', 'Parch', 'Ticket',
'Fare', 'Sex_female', 'Embarked_C', 'Embarked_Q', 'Embarked_S',
'Cabin_Alone', 'Cabin_Double room', 'Cabin_Four person room',
'Cabin_Other', 'Cabin_Three person room', 'Pclass_1', 'Pclass_2',
'Pclass_3', 'Cabin symbol_A', 'Cabin symbol_B', 'Cabin symbol_C',
'Cabin symbol_D', 'Cabin symbol_E', 'Cabin symbol_F', 'Cabin symbol_G',
'Cabin symbol_T'],
dtype='object')df_test['Cabin symbol_T'] = 0
df_test['Cabin_Four person room'] = 0df_train = df_train.reindex(columns=['PassengerId', 'Name', 'Age', 'SibSp', 'Parch', 'Ticket',
'Fare', 'Sex_female', 'Embarked_C', 'Embarked_Q', 'Embarked_S',
'Cabin_Alone', 'Cabin_Double room', 'Cabin_Four person room',
'Cabin_Other', 'Cabin_Three person room', 'Pclass_1', 'Pclass_2',
'Pclass_3', 'Cabin symbol_A', 'Cabin symbol_B', 'Cabin symbol_C',
'Cabin symbol_D', 'Cabin symbol_E', 'Cabin symbol_F', 'Cabin symbol_G',
'Cabin symbol_T', 'Survived'])df_test = df_test.reindex(columns=['PassengerId', 'Name', 'Age', 'SibSp', 'Parch', 'Ticket',
'Fare', 'Sex_female', 'Embarked_C', 'Embarked_Q', 'Embarked_S',
'Cabin_Alone', 'Cabin_Double room', 'Cabin_Four person room',
'Cabin_Other', 'Cabin_Three person room', 'Pclass_1', 'Pclass_2',
'Pclass_3', 'Cabin symbol_A', 'Cabin symbol_B', 'Cabin symbol_C',
'Cabin symbol_D', 'Cabin symbol_E', 'Cabin symbol_F', 'Cabin symbol_G',
'Cabin symbol_T'])df_test.isna().sum()PassengerId 0 Name 0 Age 0 SibSp 0 Parch 0 Ticket 0 Fare 0 Sex_female 0 Embarked_C 0 Embarked_Q 0 Embarked_S 0 Cabin_Alone 0 Cabin_Double room 0 Cabin_Four person room 0 Cabin_Other 0 Cabin_Three person room 0 Pclass_1 0 Pclass_2 0 Pclass_3 0 Cabin symbol_A 0 Cabin symbol_B 0 Cabin symbol_C 0 Cabin symbol_D 0 Cabin symbol_E 0 Cabin symbol_F 0 Cabin symbol_G 0 Cabin symbol_T 0 dtype: int64
Views of the best correlated features
corrmat = df_train.corr()
cols = corrmat.nlargest(df_train.shape[1], 'Survived')['Survived'].index
cm = np.corrcoef(df_train[cols].values.T)
sns.set(font_scale=0.7)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 5}, yticklabels=cols.values, xticklabels=cols.values)Model
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from imblearn.over_sampling import RandomOverSampler
from sklearn.model_selection import train_test_splitX_train = df_train.drop(['PassengerId', 'Name', 'Ticket', 'Survived'], axis=1)
y_train = df_train['Survived']
X_test = df_test.drop(['PassengerId', 'Name', 'Ticket'], axis=1)ros = RandomOverSampler()
X_train, y_train = ros.fit_resample(X_train, y_train)X_tr, X_val, y_tr, y_val = train_test_split(X_train, y_train, test_size=0.2)len(y_train[y_train==1]), len(y_train[y_train==0])(549, 549)
sc = StandardScaler()
X_train_std = sc.fit_transform(X_tr)
X_val = sc.transform(X_val)clf = LogisticRegression()
clf.fit(X_train_std, y_tr)
clf.score(X_val, y_val)0.7818181818181819
X_test = sc.transform(X_test)
predictions = clf.predict(X_test)submissionStacking = pd.DataFrame({ 'PassengerId': df_test["PassengerId"],'Survived': predictions })
submissionStacking.to_csv("submission.csv", index=False)