BIDA PRACTICAL

Practical 3

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.Perform the data classification using classification algorithm using R/Python.

rainfall <- c(799,1174.8,865.1,1334.6,635.4,918.5,685.5,998.6,784.2,985,882.8,1071) rainfall.timeseries<-ts(rainfall, start = c(2012,1) ,frequency = 12) print(rainfall.timeseries) png(file = "rainfall.png") plot(rainfall.timeseries) dev.off() plot(rainfall.timeseries)

Practical 4

Perform the data clustering using clustering algorithm using R/Python

newiris <- iris newiris$Species <- NULL (kc<-kmeans(newiris,3)) print(kc) table (iris$Species,kc$cluster) plot(newiris[c("Sepal.Length", "Sepal.Width")],col=kc$cluster) points(kc$centers[,c("Sepal.Length","Sepal.Width")],col=1:3,pch=8,cex=2)

Practical - 5

.Perform the Linear regression on the given data warehouse data using R/Python.

x<-c(151, 174, 138, 186, 128, 136, 179, 163, 152, 131) y<-c(63, 81, 56, 91, 47, 57, 76, 72, 62, 48) relation<-lm(y~x) a<-data.frame(x=140) result<-predict(relation,a) print(result) png(file= "linearreg.png") plot(y,x,col="blue", main="weight & height regression",abline(lm(x~y)), cex=1.3, pch=16) dev.off() plot(y,x,col="blue", main="weight & height regression",abline(lm(x~y)), cex=1.3, pch=16) print(summary(relation))

Practical 6

Perform the logistic regression on the given data warehouse data using R/Python

input<- mtcars[,c("am","cyl","hp","wt")] print(head(input)) am.data=glm(formula = am~cyl+hp+wt,data=input,family = binomial) print(summary(am.data))

Practical 7

Write a Python program to read data from a CSV file, perform simple data analysis, and generate basic insights. (Use Pandas is a Python library)

1. Male to Female ratio on the Titanic: import pandas as pd df= pd.read_csv('train.csv') print(df.columns) print(df['Sex'].value_counts()) print(df['Sex'].value_counts(normalize=True)) 2. Surviving male to female ratio on the Titanic: import pandas as pd df= pd.read_csv('train.csv') print(df.columns) print(df[df["Survived"]==1]['Sex'].value_counts()) print(df[df["Survived"] == 1]['Sex'].value_counts(normalize=True)) 3. Median age of each sex: import pandas as pd df= pd.read_csv('train.csv') median_age_men=df[df['Sex']=='male']['Age'].median() median_age_women=df[df['Sex']=='female']['Age'].median() print(f"The median age of men is {median_age_men}") print(f"The median age of women is {median_age_women}")

Practical 8

A) Perform data visualization using Python on any sales data

1. Scatter Plot: Scatter plots are used to observe relationships between variables and uses dots to represent the relationship between them import pandas as pd import matplotlib.pyplot as plt data=pd.read_csv("tips.csv") plt.scatter(data['day'], data['tip'], c=data['size'], s=data['total_bill']) plt.title("scatter plot") plt.xlabel('Day') plt.ylabel('Tip') plt.colorbar() plt.show() 2. Line Chart Line Chart is used to represent a relationship between two data X and Y on a different axis import pandas as pd import matplotlib.pyplot as plt data = pd.read_csv("tips.csv") plt.plot(data['tip']) plt.plot(data['size']) plt.title("Scatter Plot") plt.xlabel('Day') plt.ylabel('Tip') plt.show() 3.Bar Chart A bar plot or bar chart is a graph that represents the category of data with rectangular bars with lengths and heights that is proportional to the values which they represent import pandas as pd import matplotlib.pyplot as plt data=pd.read_csv("tips.csv") plt.bar(data['day'], data['tip']) plt.title("Bar Chart") plt.xlabel('Day') plt.ylabel('Tip') plt.show() 4. Histogram A histogram is basically used to represent data in the form of some groups import pandas as pd import matplotlib.pyplot as plt data = pd.read_csv("tips.csv") plt.hist(data['total_bill']) plt.title("Histogram") plt.show() 5. Pie Chart A Pie Chart is a circular statistical plot that can display only one series of data. The area of the chart is the total percentage of the given data import pandas as pd import matplotlib.pyplot as plt df = pd.read_csv("tips.csv") count_Sun = df['day'].value_counts().get('Sun', 0) count_Mon = df['day'].value_counts().get('Mon', 0) count_Tue = df['day'].value_counts().get('Tue', 0) count_Wed = df['day'].value_counts().get('Wed', 0) count_Thu = df['day'].value_counts().get('Thur', 0) count_Fri = df['day'].value_counts().get('Fri', 0) count_Sat = df['day'].value_counts().get('Sat', 0) print("Occurrences of 'Sun':", count_Sun) print("Occurrences of 'Mon':", count_Mon) print("Occurrences of 'Tue':", count_Tue) print("Occurrences of 'Wed':", count_Wed) print("Occurrences of 'Thu':", count_Thu) print("Occurrences of 'Fri':", count_Fri) print("Occurrences of 'Sat':", count_Sat) mylabel = ["Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat"] e=(0.2, 0, 0, 0,0,0,0) arr = [count_Sun, count_Mon, count_Tue, count_Wed, count_Thu, count_Fri, count_Sat] plt.pie(arr, labels=mylabel, explode=e, startangle=140) plt.title("Pie Chart of Occurrences by Day") plt.show()
 

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