Dear,

please note this project is about the creation of PHP and MySQL based web application in which you will be required to upload the given data into MySQL database using PHP.You are required to fulfill the following requirements.

Download the XAMPP server by following the link.

Download XAMPP (apachefriends.org)

1.Install XAMPP on your laptop/computer

2.Go to Kaggle Dataset or Google dataset search

https://www.kaggle.com/datasets

https://datasetsearch.research.google.com/

3.Download any dataset as a CSV file

4.Import CSV File into MySQL using PHP

5.Create an HTML form.

6.Write PHP script for connecting to XAMPP and MySQL database.

7.Execute MySQL query from php using HTML form.

8.Create Dynamic Data Visualization with PHP and MySQL.

9.Create users Registration and Login System with PHP and MySQL.

The project will have two sub-parts namely “report” of 5 absolute marks and “presentation” of 10 absolute marks.

Moreover, your kindly requested for prepare presentation for this project.

Best Regards.

Choose a Business Case (dataset should be large enough for accurate model building) for data analysis and model building for prediction/Classification/ clustering etc.. (you can use

all the ML algorithms). Before doing the analysis, data cleaning steps must be included.

2. You can choose any domain dataset

3. There must be at least 6 – 7 insights/observations taken from the dataset and each observation should have a proper reading/ explanation.

4. You must submit a single notebook file and a dataset file(csv file)

5. (Name, section, etc…) should be included in the jupyter notebook file only

6. mention the problem statement in the heading on .ipynb file

This is how your notebook file is supposed to be:

1. Give your name,ID
2. Give a project description at the heading
3. Problem statement
4. About the dataset
5. source of the data
6. data preparation
7. Algorithm
8. What you are expecting from the output/ model
9. Every graph/ visual should have an insight (mention about it, what you read from the graph), and how you are using it for further analysis.
10. Model building
11. model evaluation
12. Conclusion

Part 1:

– Writing and testing your own implementation of k-means on a simple data (IRIS) (Follow the description attached for more details)

Part 2:

– The MNIST data is provided under Test and consists of 10,000 images of handwritten digits (0-9). The images were scanned and scaled into 28×28 pixels. For every digit, each pixel can be represented as an integer in the range [0, 255] where 0 corresponds to the pixel being completely white, and 255 corresponds to the pixel being completely black. This gives us a 28×28 matrix of integers for each digit. We can then flatten each matrix into a 1×784 vector. No labels are provided.

– Use your algorithm from Part 1 to assign each of the instances in the input data to K clusters identified from 1 to K

– The resulting file contains 1 collum of 10,000 rows with int from 1 to 10.

Iris data:

https://archive.ics.uci.edu/ml/machine-learning-da…

MNIST data: Attachement (testData)

For this assignment, you will perform a mutation analysis study for an open–source software project. You will use the PIT mutation framework for Java to perform the mutation analysis.

The project you will analyze is JFreeChart, which has been in this git link https://github.com/Bindu909/jfreechart-mutation-an…

After the execution of PIT is complete, you should submit the following files for this assignment:

(1) The generated file by PIT (this I have already generated)

(2) One written report to describe and analyze your results.

In reporting your results, you should include :

• The number of all mutants generated
• The number of mutantscovered by the test suite
• The number of mutantskilled by the test suite
• The number of live mutants
• The overall mutation score of the test suite

Discuss and explain your observed results.

• For the generated mutants, does killed + live = covered? Why or why not?
• Do you have any other interesting insights or opinions on the experience

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Project 3:Project 3 Three js Project Overview In this project you will create a unique 3D animated scene composed of Three.js graphic components. The scene should include animation, lighting and multiple objects. Requirements: 1. Using Three.js create a unique 3D animated scene. The scene has the following specifications: a. Size: minimum of 640×480 b. Includes at least 6 different shapes c. Uses multiple lighting effects d. Includes radio buttons, slider bars or other widgets to turn on or off certain components of the animation. 2. Use Three.js 3. All JavaScript source code should be written using Google JavaScript style guide.( http://google.github.io/styleguide/jsguide.html) 4. Prepare, conduct and document a test plan verifying your application is working as expected. This plan should include a test matrix listing each method you tested, how you tested it, and the results of testing