gifty003 / exno2ds

EXNO2DS

AIM:

  To perform Exploratory Data Analysis 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 to perform Data Cleansing,Removing Outliers and Exploratory Data Analysis.

STEP 2: Replace the null value using any one of the method from mode,median and mean based on the dataset available.

STEP 3: Use boxplot method to analyze the outliers of the given dataset.

STEP 4: Remove the outliers using Inter Quantile Range method.

STEP 5: Use Countplot method to analyze in a graphical method for categorical data.

STEP 6: Use displot method to represent the univariate distribution of data.

STEP 7: Use cross tabulation method to quantitatively analyze the relationship between multiple variables.

STEP 8: Use heatmap method of representation to show relationships between two variables, one plotted on each axis.

CODING AND OUTPUT

Developed by : Giftson Rajarathinam N
Register no.:  212222233002

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

dt=pd.read_csv("/content/titanic_dataset.csv")
dt

dt.info()

dt.shape

dt.set_index("PassengerId",inplace=True

dt.describe()

dt.nunique()

dt["Survived"].value_counts()

per=(dt["Survived"].value_counts()/dt.shape[0]*100).round(2)
per

sns.countplot(data=dt,x="Survived")

dt

dt.Pclass.unique()

dt.rename(columns={'Sex':'Gender'},inplace=True)
dt

sns.catplot(x="Gender",col="Survived",kind="count",data=dt,height=5, aspect=.7)

sns.catplot(x='Survived',hue="Gender",data=dt,kind="count")

dt.boxplot(column="Age",by="Survived")

sns.scatterplot(x=dt["Age"],y=dt["Fare"])

sns.jointplot(x="Age",y="Fare",data=dt)

import matplotlib.pyplot as plt
fig, ax1=plt.subplots(figsize=(8,5))
pt=sns.boxplot(ax=ax1,x='Pclass',y='Age',hue='Gender',data=dt)

sns.catplot(data=dt,col="Survived",x="Gender",hue="Pclass",kind="count")

#co-relation
import seaborn as sns
corr=dt.corr()
sns.heatmap(corr,annot=True)

sns.pairplot(dt)

RESULT

The program was successfully executed.