Monday, August 12, 2019

CSE lab Manuals

Mathematical Foundation for Computer Science (MFCS) Notes



Click below link to download all units material of MFCS


MFCS notes

Machine Learning basics using Python


In this article you will learn the basics of machine learning using python ;such as uploading datasets and displaying dataset & its attributes. Use the dataset given below.

Numeric.csv  dataset  Click here to download.


Open colab.research.google.com



Uploading dataset using Panda
import pandas as pd     
df=pd.read_csv("numeric.csv") 
print(df)

Check the number of rows and columns present in the dataset
import pandas as pd     
df=pd.read_csv("numeric.csv") 
rows,columns=df.shape
print( “number of rows:”,rows)
print( “number of columns:”,columns)



Displaying column_names and data
import pandas as pd     
df=pd.read_csv("numeric.csv") 
rows,columns=df.shape
print( "number of rows:",rows)
print( "number of columns:",columns)
column_names=df.columns   
print(column_names)       #displays column names
data=df.values
print(data)               #displays data present in the dataset without headings



Analyzing attribute values to identify qualitative or quantitative type
import pandas as pd     
df=pd.read_csv("numeric.csv") 
rows,columns=df.shape
print( "number of rows:",rows)
print( "number of columns:",columns)
column_name=df.columns   

from collections import Counter
for i in range(columns):
  attribute_data=df[column_name[i]]
  cnt=Counter(attribute_data)
  cnt=dict(cnt)
  print("attribute " ,(i+1),cnt)            # to display frequency of values in each attribute.
  print("value range", attribute_data.unique())


Finding Outliers
import pandas as pd     
df=pd.read_csv("numeric.csv") 
rows,columns=df.shape
print( "number of rows:",rows)
print( "number of columns:",columns)
column_name=df.columns   

from collections import Counter
for i in range(columns):
  attribute_data=df[column_name[i]]
  cnt=Counter(attribute_data)
  cnt=dict(cnt)
  print("attribute " ,(i+1),cnt)            # to display frequency of values in each attribute.
Import seaborn as sns
Sns.boxplot(x=df[column_name[0]])    # to draw boxplot for first column. Here, x denotes x axis


You can see 5 outliers in the boxplot. These values are having less frequency in the first column.
To remove outliers, we can use following approaches.

  • Z score approach
  • IQR approach

First we have to identify outliers to remove them.
For this, we can use heatmap.

import pandas as pd
df=pd.read_csv("numeric.csv") 
rows,columns=df.shape
print( "number of rows:",rows)
print( "number of columns:",columns)
column_name=df.columns   

from collections import Counter
for i in range(columns):
  attribute_data=df[column_name[i]]
  cnt=Counter(attribute_data)
  cnt=dict(cnt)
  print("attribute " ,(i+1),cnt)
  print("value range", attribute_data.unique())
  import seaborn as Sns
Sns.boxplot(x=df[column_name[0]])
Sns.heatmap(df,cbar=False)


We can also use following to find outliers.. but you have to import suitable modules before using them.
import pandas as pd
df=pd.read_csv("numeric.csv") 
rows,columns=df.shape
print( "number of rows:",rows)
print( "number of columns:",columns)
column_name=df.columns   

from collections import Counter
for i in range(columns):
  attribute_data=df[column_name[i]]
  cnt=Counter(attribute_data)
  cnt=dict(cnt)
  print("attribute " ,(i+1),cnt)
  print("value range", attribute_data.unique())

import seaborn as Sns
Sns.boxplot(x=df[column_name[0]])
Sns.heatmap(df,cbar=False)

import missingno as msno
msno.matrix(df)


We can also use the following..
msno.dendrogram(df)

Thank you...

Monday, August 5, 2019

DBMS Notes


Download DBMS study materials below. SCROLL DOWN FOR MY VIDEO LECTURES ON DBMS.
UNIT 1
UNIT 2
UNIT 3 



DBMS PPT SLIDES

You can download the ppt slides for DBMS from below links. 

UNIT 1 PPT

UNIT 2 PPT

UNIT 3 PPT

UNIT 4 PPT

UNIT 5 PPT


UNIT 1 VIDEOS 


PART 1:

PART 2:

PART 3:


UNIT 2 VIDEOS

PART 1


PART 2


PART 3


PART 4


UNIT 3 VIDEOS

PART 1


PART 2


PART 3




UNIT 4 VIDEOS

PART 1


PART 2



Saturday, December 8, 2018

FRONT END WEB TECHNOLOGIES(FEWT)




UNIT 1 NOTES                                          FEWT SYLLABUS


UNIT 2 NOTES                                          FEWT LESSON PLAN

UNIT 3 NOTES                                          MODEL QUESTION PAPER

UNIT 4 NOTES                                           VIVA QUESTIONS    

UNIT 5 NOTES                                           FEWT LAB SYLLABUS

UNIT 6 NOTES                                            FEWT LAB MANUAL


UNIT 1 PPT                                           

UNIT 2  PPT


UNIT 3  PPT

UNIT 4  PPT

UNIT 5  PPT

UNIT 6  PPT

Tuesday, November 6, 2018

Python Programming


 
  • Python started in the late 1980s.
  • The implementation of Python was started in the 1989 by Guido Van Rossum at CWI in Netherland.
  • On December 3, 2008, Python 3.0  was released. it is also known as "Py3K".
  • Python is a general purpose, dynamic, high level and interpreted programming language. It supports Object Oriented programming approach to develop applications. It is simple and easy to learn and provides lots of high-level data structures.
    Python is easy to learn yet powerful and versatile scripting language which makes it attractive for Application Development.
  • We don't need to use data types to declare variable because it is dynamically typed so we can write a=10 to assign an integer value in an integer variable.
  • example
                   print("hello friends")  
          used to display the message hello friends.