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import matplotlib.pyplot as plt

import pandas as pd

import seaborn as sns

import numpy as np
import pandas as pd
import seaborn as sns
sns.set()
import matplotlib.pyplot as plt
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier

train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')

df = pd.concat([train, test], axis=0).reset_index(drop=True)

df = df.drop(['PassengerId', 'Survived', 'Name', 'Ticket', 'Cabin'], axis=1)

df.isnull().sum().sort_values(ascending=False) * 100 / df.shape[0]

sns.histplot(data=df['Age'], color='teal', kde=True)
plt.show()