``` In []: ```python import numpy as np import pandas as pd import matplotlib.pyplot as plt import os import re import warnings warnings.filterwarnings('ignore') ``` In []: ```python # read data df_test = pd.read_csv('test.csv') df_train = pd.read_csv('train.csv') ``` In []: ```python df_train.head() ``` Out []: ```output

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	373450	8.0500	NaN	S

``` In []: ```python df_train.info() ``` Out []: ```output RangeIndex: 891 entries, 0 to 890 Data columns (total 12 columns): # Column Non-Null Count Datetime Formats Dtype --- ------ -------------- ---------------- ----- 0 PassengerId 891 non-null object int64 1 Survived 891 non-null object int64 2 Pclass 891 non-null object int64 3 Name 891 non-null object object 4 Sex 891 non-null object object 5 Age 714 non-null object float64 6 SibSp 891 non-null object int64 7 Parch 891 non-null object int64 8 Ticket 891 non-null object object 9 Fare 891 non-null object float64 10 Cabin 204 non-null object object 11 Embarked 889 non-null object object dtypes: float64(2), int64(5), object(5) memory usage: 83.7+ KB ``` In []: ```python df_train.isna().sum() ``` Out []: ```output 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 []: ```python df_test.isna().sum() ``` Out []: ```output 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 []: # Preprocessing In []: ```python # fill missing values in Age df_train['Age'] = df_train['Age'].fillna(df_train['Age'].median()) df_test['Age'] = df_test['Age'].fillna(df_test['Age'].median()) # fill missing values in Embarked df_train['Embarked'] = df_train['Embarked'].fillna(df_train['Embarked'].mode()[0]) # fill missing values in Fare df_test['Fare'] = df_test['Fare'].fillna(df_test['Fare'].median()) # drop Cabin df_train.drop('Cabin', axis=1, inplace=True) df_test.drop('Cabin', axis=1, inplace=True) ``` In []: ```python df_train.isna().sum() ``` Out []: ```output PassengerId 0 Survived 0 Pclass 0 Name 0 Sex 0 Age 0 SibSp 0 Parch 0 Ticket 0 Fare 0 Embarked 0 dtype: int64 ``` In []: ```python df_test.isna().sum() ``` Out []: ```output PassengerId 0 Pclass 0 Name 0 Sex 0 Age 0 SibSp 0 Parch 0 Ticket 0 Fare 0 Embarked 0 dtype: int64 ``` In []: # Feature Engineering In []: ```python # create FamilySize feature df_train['FamilySize'] = df_train['SibSp'] + df_train['Parch'] + 1 df_test['FamilySize'] = df_test['SibSp'] + df_test['Parch'] + 1 # create IsAlone feature df_train['IsAlone'] = 0 df_train.loc[df_train['FamilySize'] == 1, 'IsAlone'] = 1 df_test['IsAlone'] = 0 df_test.loc[df_test['FamilySize'] == 1, 'IsAlone'] = 1 # extract title from Name def get_title(name): title_search = re.search(' ([A-Za-z]+)\.', name) if title_search: return title_search.group(1) return "" df_train['Title'] = df_train['Name'].apply(get_title) df_test['Title'] = df_test['Name'].apply(get_title) # group rare titles df_train['Title'] = df_train['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare') df_train['Title'] = df_train['Title'].replace('Mlle', 'Miss') df_train['Title'] = df_train['Title'].replace('Ms', 'Miss') df_train['Title'] = df_train['Title'].replace('Mme', 'Mrs') df_test['Title'] = df_test['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare') df_test['Title'] = df_test['Title'].replace('Mlle', 'Miss') df_test['Title'] = df_test['Title'].replace('Ms', 'Miss') df_test['Title'] = df_test['Title'].replace('Mme', 'Mrs') # encode categorical features from sklearn.preprocessing import LabelEncoder le = LabelEncoder() df_train['Sex'] = le.fit_transform(df_train['Sex']) df_test['Sex'] = le.transform(df_test['Sex']) df_train['Embarked'] = le.fit_transform(df_train['Embarked']) df_test['Embarked'] = le.transform(df_test['Embarked']) df_train['Title'] = le.fit_transform(df_train['Title']) df_test['Title'] = le.transform(df_test['Title']) ``` In []: ```python # drop unnecessary columns df_train.drop(['PassengerId', 'Name', 'Ticket'], axis=1, inplace=True) test_ids = df_test['PassengerId'] df_test.drop(['PassengerId', 'Name', 'Ticket'], axis=1, inplace=True) ``` In []: ```python df_train.head() ``` Out []: ```output

	Survived	Pclass	Sex	Age	SibSp	Fare	Embarked	FamilySize	IsAlone	Title
0	0	3	1	22.0	1	7.2500	2	2	0	2
1	1	1	0	38.0	1	71.2833	0	2	0	3
2	1	3	0	26.0	0	7.9250	2	1	1	1
3	1	1	0	35.0	1	53.1000	2	2	0	3
4	0	3	1	35.0	0	8.0500	2	1	1	2

``` In []: # Modeling In []: ```python from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import GridSearchCV X = df_train.drop('Survived', axis=1) y = df_train['Survived'] param_grid = { 'n_estimators': [100, 200, 300], 'max_depth': [None, 5, 10], 'min_samples_split': [2, 5, 10

Sisekumm Michelin Heavy Duty

Kirjeldus

Tehnilised andmed

Klientide arvustused (46)