<jupyter_start><jupyter_text>Import the necessary Libraries and Packages:<jupyter_code>import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline<jupyter_output>/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
  import pandas.util.testing as tm<jupyter_text>Load the Dataset:<jupyter_code>train=pd.read_csv('/content/drive/My Drive/train.csv')
train.head()<jupyter_output><empty_output><jupyter_text>Univariate EDA:  What is the Count of Survived vs Not Survived?<jupyter_code>print(train["Survived"].value_counts())<jupyter_output>0    549
1    342
Name: Survived, dtype: int64<jupyter_text>Find out the Numerical Columns Basic Statistics:<jupyter_code>train.describe()<jupyter_output><empty_output><jupyter_text>Visualize Survived vs Not Survived:<jupyter_code>sns.countplot(x='Survived', data=train)<jupyter_output><empty_output><jupyter_text>Visual EDA for single Categorical Column: "Embarked"<jupyter_code>sns.countplot(x='Embarked', data=train)<jupyter_output><empty_output><jupyter_text>Visual EDA for single Continuous Column: "Fare" using Distribution Plot<jupyter_code>sns.distplot(train.Fare, kde=False)<jupyter_output><empty_output><jupyter_text>Visual EDA for single Continuous Column: "Fare" using KDE(Kernel Density Estimation) Plot<jupyter_code>sns.distplot(train.Fare, kde=True, hist=False)<jupyter_output><empty_output><jupyter_text>Bivariate EDA:   What is the count of Males and Females Survived and Not Survived in each Class? Males<jupyter_code>print(train["Survived"][train["Sex"] == 'male'].value_counts())<jupyter_output>0    468
1    109
Name: Survived, dtype: int64<jupyter_text>Females<jupyter_code>print(train["Survived"][train["Sex"] == 'female'].value_counts())<jupyter_output>1    233
0     81
Name: Survived, dtype: int64<jupyter_text>Visualize Survived and Not Survived with respect to the 'Embarked' Column:<jupyter_code>sns.factorplot(x='Survived', col='Embarked', kind='count', data=train)<jupyter_output><empty_output><jupyter_text>Plot a Desnity Graph based on Fare and Survival Rate:<jupyter_code>train.groupby('Survived').Fare.hist(alpha=0.6)<jupyter_output><empty_output><jupyter_text>Plot a Distplot based on Fare and Survival Rate:<jupyter_code><jupyter_output><empty_output><jupyter_text>How are "Age" and "Fare" Columns related? Plot a Graph for the same:<jupyter_code>sns.stripplot(x='Age', y='Fare', data=train)<jupyter_output><empty_output><jupyter_text>Multivariate EDA:  Does Age have an impact on Survival Rate for each Sex and Class group?<jupyter_code>sns.violinplot(x ="Sex", y ="Age", hue ="Survived",  
data = train, split = True) 
sns.violinplot(x ="Pclass", y ="Age", hue ="Survived",  
data = train, split = True)<jupyter_output><empty_output><jupyter_text>Plot a HEATMAP showing the correlations between different features:<jupyter_code>sns.heatmap(train.corr(), fmt = ".2f")<jupyter_output><empty_output>
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