pavivenkat614 / ex-04-multivariate-analysis

                                                          Ex-04-Multivariate-Analysis

AIM: To apply multivariate analysis on a give data set.

Algorithm:

1.start

2.read the dta from the file

3.clean the data a)remove outliers b)fill the null value or drop the column

4.perform multivariate analysis a)scatter plot b)box plot c)heat map

Program: read and cleaned the null value: code: import pandas as pd

df=pd.read_csv("/content/SuperStore.csv")

df['Postal Code']=df['Postal Code'].fillna(df['Postal Code'].mode()[0])

df.isnull().sum()

scatter plot:

df.dtypes:

code:

df.dtype

output:

kurtosis:

df.kurtosis()

code:

output:

scatter plot:

code:

import pandas as pd

import seaborn as sns

sns.boxplot(df['Sales'],df['Postal Code'])

output:

Bar plot:

code:

sns.barplot(x=df["Sales"],y=df["Postal Code"],data=df)

bar plot

code:

import pandas as pd

import seaborn as sns

import matplotlib.pyplot as plt

states=df.loc[:,["Postal Code","Sales"]]

states=states.groupby(by=["Postal Code"]).sum().sort_values(by="Sales")

plt.figure(figsize=(17,7))

sns.barplot(x=states.index,y="Sales",data=states)

plt.xticks(rotation = 90)

plt.xlabel=("Postal Code")

plt.ylabel=("Sales")

plt.show()

output:

code:

import pandas as pd

import seaborn as sns import matplotlib.pyplot as plt

states=df.loc[:,["State","Postal Code"]]

states=states.groupby(by=["State"]).sum().sort_values(by="Postal Code")

plt.figure(figsize=(17,7))

sns.barplot(x=states.index,y="Postal Code",data=states)

plt.xticks(rotation = 90)

plt.xlabel=("Sales")

plt.ylabel=("Postal Code")

plt.show()

output:

corr:

df.corr()

output:

Heatmap:

code:

import numpy as np

import seaborn as sn

import matplotlib.pyplot as plt

data=pd.read_csv("/content/SuperStore.csv")

data = np.random.randint(low = 1, high = 100, size = (10, 10))

print("The data to be plotted:\n")

print(data)

hm = sn.heatmap(data = data)

plt.show()

output:

RESULT:

Thus we have applied the multiivariate analysis sucessfully