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BE COMPUTER SPPU LP1 Programs


LP1 Lab Programs



Department: Computer Engineering                                    Class: B.E Semester: VII



Subject: Laboratory Practice I (410246) 


Download LP1 Programs 

Download LP1 Program Set 2- Thanks to Amruta Kulkarni

Part 1: HPC Programs

1

Test for input N and generate a randomized vector V of length N (N should be large). The program should generate output as the two computed maximum values as well as the time taken to find each value.
2 3. Multiply two N × N arrays using n2 processors
3 For Bubble Sort and Merger Sort, based on existing sequential algorithms, design and implement parallel algorithm utilizing all resources available.
4
Parallel Search Algorithm-(MPI)
Design and implement parallel algorithm utilizing all resources available. for
Breadth-First Search ( tree or an undirected graph) OR
 Best-First Search that ( traversal of graph to reach a target in the shortest possible path)


Download HPC_LAB

Part II: AIR LAB



1


Solve 8-puzzle problem using A* algorithm. Assume any initial configuration and define goal configuration clearly.
1. JAVA Implementation


2



Implement alpha-beta pruning graphically with proper example and justify the pruning.
1. Python Implementation

3


Develop elementary chatbot for suggesting investment as per the customers need.
1. Java Implementation

4



Constraint Satisfaction Problem:

Implement crypt-arithmetic problem or n-queens or graph coloring problem ( Branch and Bound and Backtracking) 

Note.: CPP is not allowed for final Practicals.

5


Implement goal stack planning for the following configurations from the blocks world,
6

Use Heuristic Search Techniques to Implement Hill-Climbing Algorithm.


Part III: Data Analytics LAB


No. Problem Statement

1


Download the Iris flower dataset or any other dataset into a DataFrame. (eg https://archive.ics.uci.edu/ml/datasets/Iris ) Use Python/R and Perform following –
How many features are there and what are their types (e.g., numeric, nominal)?
Compute and display summary statistics for each feature available in the dataset. (eg. minimum value, maximum value, mean, range, standard deviation, variance and percentiles
Data Visualization-Create a histogram for each feature in the dataset to illustrate the feature distributions. Plot each histogram.
Create a boxplot for each feature in the dataset. All of the boxplots should be combined into a single plot. Compare distributions and identify outliers.


2


Download Pima Indians Diabetes dataset. Use Naive Bayes‟ Algorithm for classification
Load the data from CSV file and split it into training and test datasets.
summarize the properties in the training dataset so that we can calculate probabilities and make predictions.
Classify samples from a test dataset and a summarized training dataset.


3


Write a Hadoop program that counts the number of occurrences of each word in a text file.  
4

Trip History Analysis: Use trip history dataset that is from a bike sharing service in the United States. The data is provided quarter-wise from 2010 (Q4) onwards. Each file has 7 columns. Predict the class of user. Sample Test data set available here https://www.capitalbikeshare.com/trip-history-data

DOWNLOAD DA ALL PROGRAMS

P.S. Please double check programs for correct algorithm and implementation.

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