https://github.com/Dinesh7318/Ex-08-Data-Visualization-.git

https://colab.research.google.com/drive/111136Er3pxtD6YhPS6rTQUA735xFzCgn?usp=sharing

To Perform Data Visualization on a complex dataset and save the data to a file.

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

Read the given Data

Clean the Data Set using Data Cleaning Process

Apply Feature generation and selection techniques to all the features of the data set

Apply data visualization techniques to identify the patterns of the data.

import pandas as pd df=pd.read_csv("Superstore.csv",encoding='unicode_escape')

df.head()

import seaborn as sns from matplotlib import pyplot as plt

plt.figure(figsize=(9,6)) sns.lineplot(x="Segment",y="Region",data=df,marker='o') plt.xticks(rotation = 90)

sns.lineplot(x='Ship Mode',y='Category', hue ="Segment",data=df)

sns.lineplot(x="Category",y="Sales",data=df,marker='o')

sns.scatterplot(x='Category',y='Sub-Category',data=df)

sns.scatterplot(x='Category', y='Sub-Category', hue ="Segment",data=df)

plt.figure(figsize=(10,7)) sns.scatterplot(x="Region",y="Sales",data=df) plt.xticks(rotation = 90)

sns.boxplot(x="Sub-Category",y="Discount",data=df)

sns.boxplot( x="Profit", y="Category",data=df)

sns.violinplot(x="Profit",data=df)

sns.barplot(x="Sub-Category",y="Sales",data=df) plt.xticks(rotation = 90)

sns.barplot(x="Category",y="Sales",data=df) plt.xticks(rotation = 90)

sns.pointplot(x=df["Quantity"],y=df["Discount"])

sns.countplot(x="Category",data=df)

sns.countplot(x="Sub-Category",data=df)

sns.histplot(data=df,x ='Ship Mode',hue='Sub-Category')

sns.kdeplot(x="Profit", data = df,hue='Category')

plt.plot(df['Category'], df['Sales']) plt.show()

df.corr() plt.subplots(figsize=(12,7)) sns.heatmap(df.corr(),annot=True)

df1=df.groupby(by=["Ship Mode"]).sum() labels=[] for i in df1.index: labels.append(i) colors=sns.color_palette("bright") plt.pie(df1["Sales"],labels=labels,autopct="%0.0f%%") plt.show()

df3=df.groupby(by=["Category"]).sum() labels=[] for i in df3.index: labels.append(i) plt.figure(figsize=(8,8)) colors = sns.color_palette('pastel') plt.pie(df3["Profit"],colors = colors,labels=labels, autopct = '%0.0f%%') plt.show()

plt.hist(df["Sub-Category"],facecolor="peru",edgecolor="blue",bins=10) plt.show()

plt.bar(df.index,df['Category']) plt.show()

plt.scatter(df["Region"],df["Profit"], c ="blue") plt.show()

plt.boxplot(x="Sales",data=df) plt.show()

RESULT: Hence, Data Visualization is applied on the complex dataset using libraries like Seaborn and Matplotlib successfully and the data is saved to file.