BIDA PRACTICALS

 Full File

Only Codes Files

Txt Codes

Practical No. 3 : Aim: Perform the data classification using classification algorithm using R/Python

Python CODE:

import numpy as np

import matplotlib.pyplot as plt

rainfall = [799, 1174.8, 865.1, 1334.6, 635.4, 918.5, 

685.5, 998.6,

784.2, 985, 882.8, 1071]

months = np.arange("2012-01", "2013-01", 

dtype="datetime64[M]")

plt.figure()

plt.plot(months, rainfall, marker='o')

plt.title("Monthly Rainfall (2012)")

plt.xlabel("Month")

plt.ylabel("Rainfall (mm)")

plt.grid(True)

plt.savefig("rainfall.png")

plt.close()

plt.figure()

plt.plot(months, rainfall, marker='o')

plt.title("Monthly Rainfall (2012)")

plt.xlabel("Month")

plt.ylabel("Rainfall (mm)")

plt.grid(True)

plt.show()

------------------

PRACTICAL NO: 4 Aim: Perform the data clustering using clustering algorithm using R/python

R Code:

newiris <- iris

newiris$Species <- NULL

(kc <- kmeans(newiris,3))

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) 

dev.off()

plot(newiris[c("Sepal.Length","Sepal.Width")],col=kc$cluster)

------------------

PRACTICAL NO:7 Aim: Write a python program to read data from csv file, perform simple data analysis and generate basic insights (use pandas as a python library)

Step 1:

>py D:/bi/get-pip.py

>pip install pandas

CODE:

import pandas as pd

df = pd.read_csv(r"C:\Users\yadav\Desktop\TYIT Study 

Material\SEM 6th\BI\quality.csv")

print("dataset before skipping rows:")

print(df)

df = pd.read_csv(r"C:\Users\yadav\Desktop\TYIT Study 

Material\SEM 6th\BI\quality.csv", skiprows=[4,5])

print("Dataset after skipping rows:")

print(df)

print(df.InpatientDays)

print(df.PoorCare)

------------------

PRACTICAL NO:9 Aim: Perform data visualization using Python on any sales data.

PREREQUISITES:

>pip install pandas

>pip install matplotlib

>python -m pip install seaborn

>pip install plotly

CODE:

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

import plotly.express as px

data = {

'Date': pd.date_range(start='2024-01-01', periods=10, freq='D'),

'Product': ['A', 'B', 'C', 'A', 'B', 'C', 'A', 'B', 'C', 'A'],

'Sales': [100, 150, 200, 120, 180, 220, 140, 160, 240, 180],

'Revenue': [1000, 1500, 2000, 1200, 1800, 2200, 1400, 1600, 2400, 1800]

}

df = pd.DataFrame(data)

print(df.head())

plt.figure(figsize=(10,5))

sns.lineplot(x='Date', y='Sales', data=df, marker='o', hue='Product')

plt.title("Sales Trend Over Time")

plt.xticks(rotation=45)

plt.show()

plt.figure(figsize=(8,5))

sns.barplot(x='Product', y='Sales', data=df, estimator=sum, palette="coolwarm")

plt.title("Total Sales per Product")

plt.show()

plt.figure(figsize=(6,6))

df.groupby('Product')['Sales'].sum().plot(kind='pie', autopct='%1.1f%%', 

colormap="viridis")

plt.title("Sales Distribution by Product")

plt.ylabel("")

plt.show()

fig = px.bar(df, x='Date', y='Sales', color='Product', title="Interactive Sales 

Trend")

fig.show()

------------------

PRACTICAL NO. 5 Perform the linear regression the given data warehouse data using R/Python

R- Code :

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)

print(relation)

print(summary(relation))

a <- data.frame(x = 170)

result <- predict(relation,a)

print(result)

png(file = "linearregression.png")

plot(y,x,col = "blue",main = "Height & Weight 

Regression",

abline(lm(x~y)),cex = 1.3,pch = 16,xlab = "Weight in 

Kg",ylab = "Height in cm")

dev.off()

plot

(y,x,col = "blue",main = "Height & Weight Regression", abline(lm(x~y)),cex = 1.3,pch = 16,xlab = "Weight in Kg",ylab = "Height in cm")