Ex-03EDA

AIM

To perform EDA on the given data set.

Explanation

The primary aim with exploratory analysis is to examine the data for distribution, outliers and anomalies to direct specific testing of your hypothesis.

ALGORITHM

STEP 1

Import the required packages.

STEP 2

Read the csv file and convert into DataFrame.

STEP 3

Perform Data Cleaning on the DataSet.

STEP 4

Detect and Remove the Outliers from the Dataset.

STEP 5

Perform Exploratory Data Analysis on the data.

CODE

import pandas as pd
import numpy as np
import seaborn as sns
df=pd.read_csv("titanic_dataset.csv")
df.info()
df.head()
df.isnull().sum()
df.drop("Cabin",axis=1,inplace=True)
df.info()
df.isnull().sum()
df["Age"]=df["Age"].fillna(df["Age"].median())
df.boxplot()
df.isnull().sum()
df["Embarked"]=df["Embarked"].fillna(df["Embarked"].mode()[0])
df.isnull().sum()
df.boxplot()
Q1 = df.quantile(0.25)
Q3 = df.quantile(0.75)
IQR = Q3 - Q1
print(IQR)
df_out = df[~((df < (Q1 - 1.5 * IQR)) |(df > (Q3 + 1.5 * IQR))).any(axis=1)]
print(df_out.shape)
df_out.boxplot()
df_out.info()
df["Embarked"].value_counts()
df_out["Embarked"].value_counts()
df["Pclass"].value_counts()
df_out["Pclass"].value_counts()
df["Survived"].value_counts()
df_out["Survived"].value_counts()
df["Sex"].value_counts()
df_out["Sex"].value_counts()
df["SibSp"].value_counts()
df_out["SibSp"].value_counts()
sns.countplot(x="Survived",data=df_out)
sns.countplot(x="Pclass",data=df_out)
sns.countplot(x="Sex",data=df_out)
sns.countplot(x="Embarked",data=df_out)
sns.countplot(x="SibSp",data=df_out)
df_out.info()
sns.displot(df_out["Fare"])
sns.displot(df_out["Age"])
sns.countplot(x="Pclass",hue="Survived",data=df_out)
sns.countplot(x="Sex",hue="Survived",data=df_out)
sns.countplot(x="SibSp",hue="Sex",data=df_out)
sns.countplot(x="Embarked",hue="Pclass",data=df_out)
sns.countplot(x="Survived",hue="Embarked",data=df_out)
sns.displot(df_out[df_out["Survived"]==0]["Age"])
sns.displot(df_out[df_out["Survived"]==1]["Age"])
sns.displot(df_out[df_out["SibSp"]==0]["Embarked"])
sns.displot(df_out[df_out["SibSp"]==1]["Embarked"])
pd.crosstab(df_out["Pclass"],df_out["Survived"])
pd.crosstab(df_out["Sex"],df_out["Survived"])
pd.crosstab(df_out["Pclass"],df_out["Sex"])
pd.crosstab(df_out["Sex"],df_out["Embarked"])
df.corr()
df_out.corr()
sns.heatmap(df.corr(),annot=True)
sns.heatmap(df_out.corr(),annot=True)