Jhansi Rani Kunaparaju

Types of hash functions

Tree: The data in a tree are not stored in a sequential manner i.e., they are not stored linearly. Instead, they are arranged on multiple levels or we can say it is a hierarchical structure. For this reason, the tree is considered to be a non-linear data structure. KeyConcepts : Nodes : Individual units within the tree, each storing data and potentially linking to other nodes. Edges : Connections between nodes, representing relationships (parent-child, sibling, etc.). Root : The topmost node in the tree, from which all other nodes originate. Parent : A node that has one or more child nodes. Child : A node connected to a parent node. Leaf : A node with no children. Subtree : A portion of a tree that is itself a tree. Representation of tree: Binary search tree :

UNIT-I : Introduction to Programming and Problem Solving : History of Computers, Basic organization of a computer: ALU, input-output units, memory, program counter, Introduction to Programming Languages, Basics of a Computer Program- Algorithms, flowcharts (Using Dia Tool), pseudo code. Introduction to Compilation and Execution, Primitive Data Types, Variables, and Constants, Basic Input and Output, Operations, Type Conversion, and Casting. Problem-solving techniques: Algorithmic approach, characteristics of algorithm, Problem solving strategies: Top-down approach, Bottom-up approach, Time and space complexities of algorithms. Unit-1 material UNIT-II : Control Structures : Simple sequential programs Conditional Statements (if, if-else, switch), Loops (for, while, do- while) Break and Continue. Unit-2 material UNIT-III : Arrays and Strings : Arrays indexing, memory model, programs with array of integers, two dimensional arrays, Introduction to Strings. Unit-3 m...

UNIT –I: Data Warehousing and Online Analytical Processing: Data Warehouse: Basic concepts, Data Warehouse Modelling: Data Cube and OLAP, Data Warehouse Design and Usage, Data Warehouse Implementation, Introduction: Why and What is data mining, What kinds of data need to be mined and patterns can be mined, Which technologies are used, Which kinds of applications are targeted. Unit-1 material UNIT –II: Data Pre-processing: An Overview, Data Cleaning, Data Integration, Data Reduction, Data Transformation and Data Discretization. UNIT –III: Classification: Basic Concepts, General Approach to solving a classification problem, Decision Tree Induction: Attribute Selection Measures, Tree Pruning, Scalability and Decision Tree Induction, Visual Mining for Decision Tree Induction. Unit-3 Material UNIT –IV: Association Analysis: Problem Definition, Frequent Item set Generation, Rule Generation: Confident Based Pruning, Rule Generation in Apriori Algorithm, Compact Representation of ...

11. Write a program for Naive Bayesian Classification in Python import pandas as pd import numpy as np from sklearn import datasets iris = datasets.load_iris() # importing the dataset iris.data # showing the iris data X=iris.data #assign the data to the X y=iris.target #assign the target/flower type to the y print (X.shape) print (y.shape) fromsklearn.model_selection import train_test_split X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=9) fromsklearn.naive_bayes import GaussianNB nv = GaussianNB() # create a classifier nv.fit(X_train,y_train) # fitting the data fromsklearn.metrics import accuracy_score y_pred = nv.predict(X_test) # store the prediction data accuracy_score(y_test,y_pred) # calculate the accuracy

  10. Write a program to calculate chi-square value using Python. Report your observation. import math a=[[ 250,200],    [50,1000]] print("Elements in dataset a are:\n"); for i in a:     for j in i:         print(j,end = " ")     print() row1tot= a[0][0]+a[0][1]; row2tot=a[1][0]+a[1][1]; col1tot=a[0][0]+a[1][0]; col2tot=a[0][1]+a[1][1]; print("\nRow one total:",row1tot);  print("\nRow two total:",row2tot)  print("\nColumn one total:",col1tot);  print("\n Column two total:",col2tot);  totalValueallcolumns= col1tot+col2tot; totalValueallrows= row1tot+row2tot; print("\nTotal value of all columns:",totalValueallcolumns);  print("\nTotal value of all rows:",totalValueallrows);  e00= (col1tot*row1tot)/totalValueallcolumns; e01= (col2tot*row1tot)/totalValueallcolumns; e10= (col1tot*row2tot)/totalValueallcolumns; e11= (col2tot*row2tot)/totalValueallcolumns; print("\n...

 Visualize the datasets using matplotlib in python.(Histogram, Bar chart, Pie chart etc.,) Histogram import matplotlib.pyplot as plt x = [1,1,2,3,3,5,7,8,9,10, 10,11,11,13,13,15,16,17,18,18, 18,19,20,21,21,23,24,24,25,25, 25,25,26,26,26,27,27,27,27,27, 29,30,30,31,33,34,34,34,35,36, 36,37,37,38,38,39,40,41,41,42, 43,44,45,45,46,47,48,48,49,50, 51,52,53,54,55,55,56,57,58,60, 61,63,64,65,66,68,70,71,72,74, 75,77,81,83,84,87,89,90,90,91 ] plt.style.use('ggplot') plt.hist(x, bins=10) plt.show() output: //// Bar Chart: import matplotlib.pyplot as plt country = ['A', 'B', 'C', 'D', 'E'] gdp_per_capita = [45000, 42000, 52000, 49000, 47000] plt.bar(country, gdp_per_capita) plt.title('Country Vs GDP Per Capita') plt.xlabel('Country') plt.ylabel('GDP Per Capita') plt.show() output:///// Pie chart: # Import libraries from matplotlib import pyplot as plt import numpy as np # Creating dataset cars = ['AUDI', 'BMW...

 6. Demonstrate knowledge flow application on data sets age specticle_prescrip astigmation tear_prod_rate contact_lenses young myopia no reduce none young myopia no normal soft young myopia yes reduce none young myopia yes normal hard young hypermertropia no reduce none young hypermertropia no normal soft young hypermertropia yes reduce none young hypermertropia yes normal hard pre_pres_byopic myopia no reduce none pre_pres_byopic myopia no normal none pre_pres_byopic myopia yes reduce soft pre_pres_byopic myopia yes normal hard pre_pres_byopic hypermertropia yes normal none pre_pres_byopic hypermertropia no reduce normal pre_pres_byopic hypermertropia yes reduce normal pre_pres_byopic myopia no normal none pre_pres_byopic myopia yes reduce none pres_byopic hypermertropia yes normal soft 1.Develop a knowledge flow layout for finding strong association rules by using, Apriori algorithm Aim: Demonstration of association rule process on contactlens.arff using apriori algorithm. ...