To perform EDA on the given data set.
The primary aim with exploratory analysis is to examine the data for distribution, outliers and anomalies to direct specific testing of your hypothesis.
Create a new folder in jupyter notebook.
Upload the given csv file and open a python kernel.
Write the codes to execute data analysis in the python kernel .
Plot the result in different methods for different Values.
End of the program.
import pandas as pd
import numpy as np
df=pd.read_csv("titanic_dataset.csv")
print(df)
df.info()
df.isnull()
df.isnull().sum()
df['Cabin']=df['Cabin'].fillna(df['Cabin'].mode()[0])
df['Age']=df['Age'].fillna(df['Age'].mode()[0])
df.head()
df.info()
df.isnull().sum()
df['Embarked']=df['Embarked'].fillna(df['Embarked'].mode()[0])
df.info()
df.isnull().sum()
import seaborn as sns
sns.countplot('Survived',data=df)
sns.countplot('Sex',data=df)
sns.countplot('Pclass',data=df)
sns.displot(df['Fare'])
sns.countplot(x='Pclass',hue='Survived',data=df)
sns.displot(df[df["Survived"]==0]['Age'])
pd.crosstab(df["Pclass"],df["Survived"])
pd.crosstab(df["Sex"],df["Survived"])
df.corr()
sns.heatmap(df.corr(),annot=True)
Thus the given dataset is analysed using EDA method.