Category: Python

Use k prototypes clustering algorithm to develop a general regression model based on cluster centroids. the visualization and analysis of clusters should be included.

You are using the housing dataset from Week 3 to perform non-hierarchical clustering.
Supported by a brief description of each of the steps and screen prints, complete the following steps:
Import the dataset into your Jupyter Notebook. Rename the Jupyter Notebook to “Clustering”
Apply the K-Means algorithm.
Perform exploratory data analysis on the data (descriptive analysis, visualization, and correlation).
Use the Elbow method to determine the number of clusters (k)
Apply the K-Means algorithm.
Append the clusters to the original dataset. Please give word file for explantion above question as well.

Optimize and fine-tune the neural network parameters (the code is
already written) to improve the frame-level speech recognition
classification using a neural network model based on input MFCC (Mel
Frequency Cepstral Coefficients) data. The task is to classify the
specific phonemes in the audio frames and eventually submit a prediction
result in .csv format to Kaggle, aiming to achieve an accuracy of 86-87%. Currently, my model achieves an accuracy of 84%.
Must use MLP (Multilayer Perceptron)
The total number of parameters up to 20 million.
The expected output/submission file should be similar to the attached submission(2).csv.
My code is HW1P2_F24_Starter_Notebook-Copy1(1).ipynb
The required data/corpus may be found at https://www.openslr.org/12.

Optimize and fine-tune the neural network parameters (the code is
already written) to improve the frame-level speech recognition
classification using a neural network model based on input MFCC (Mel
Frequency Cepstral Coefficients) data. The task is to classify the
specific phonemes in the audio frames and eventually submit a prediction
result in .csv format to Kaggle, aiming to achieve an accuracy of 86-87%. Currently, my model achieves an accuracy of 84%.
Must use MLP (Multilayer Perceptron)
The total number of parameters up to 20 million.
The expected output/submission file should be similar to the attached submission(2).csv.
My code is HW1P2_F24_Starter_Notebook-Copy1(1).ipynb
The required data/corpus may be found at https://www.openslr.org/12.

Connecting to data sources using IDE is one of the first steps in big data handling. This week, you will be using Jupyter Notebook to import and process the Boston dataset (dataset is provided). The attributes of the data are:
Attribute Information (in order):
– CRIM per capita crime rate by town
– ZN proportion of residential land zoned for lots over 25,000 sq. ft.
– INDUS proportion of non-retail business acres per town
– CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
– NOX nitric oxides concentration (parts per 10 million)
– RM average number of rooms per dwelling
– AGE proportion of owner-occupied units built before 1940
– DIS weighted distances to five Boston employment centers
– RAD index of accessibility to radial highways
– TAX full-value property-tax rate per $10,000
– PTRATIO pupil-teacher ratio by town
– B 1000(Bk – 0.63)^2 where Bk is the proportion of blacks by town
– LSTAT % lower status of the population
– MEDV Median value of owner-occupied homes in $1000’s
Complete the following steps:
Import housing data into your Jupyter Notebook
Include the shape of the data using Python’s shape method
Include the info of the data using the Python’s info method
Include the summary statistics of the data using the described method.
Include the screen print of your work with short descriptions of each method used to import and describe the dataset in your paper.

Workbook 1
Well come to the Pythoning!
Type this code import this and you can see the Zen of Python
The Zen of Python, by Tim Peters
Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren’t special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one– and preferably only one –obvious way to do it.
Although that way may not be obvious at first unless you’re Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it’s a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea — let’s do more of those!
Python I/O
Computer programs consist of commands, each command instructs the computer to take some action. A computer executes these commands one by one. Among other things, commands can be used to perform calculations, compare things in the computer’s memory, cause changes in how the program functions, relay messages or ask for information from the program’s user.
Let’s begin programming by getting familiar with the print() command, which prints text. In this context, printing essentially means that the program will show some text on the screen.
The following program will print the line “Hi there!”:
print(“Hi there!”)
When the program is run, it produces this:
Hi there!The program will not work unless the code is written exactly as it is above. For example, trying to run the print command without the quotation marks, like so
print(Hi there!)
will not print out the message, but instead causes an error:
In summary, if you want to print text, the text must all be encased in quotation marks or Python will not interpret it correctly.
Exercise 1 (1 point)
Please write a program which prints out an emoticon: 🙂
A program of multiple commands
Multiple commands written in succession will be executed in order from first to last. For example this program
print(“Welcome to Introduction to Programming!”)
print(“First we will practice using the print command.”)
print(“This program prints three lines of text on the screen.”)
prints the following lines on the screen:
Welcome to Introduction to Programming!
First we will practice using the print command.
This program prints three lines of text on the screen.
Exercise 2 (1 point)
The Mount Rushmore National Memorial is a national memorial centered on a colossal sculpture carved into the granite face of Mount Rushmore (Lakota: Tȟuŋkášila Šákpe, or Six Grandfathers) in the Black Hills near Keystone, South Dakota, United States.
This program is supposed to print the names of American presidents (left to right), but it doesn’t work quite right yet. Please fix the program so that the names are printed in the correct order.
print(“George Washington”)
print(“Theodore Roosevelt”)
print(“Abraham Lincoln”)
print(“Thomas Jefferson”)
Exercise 3 (1 point)
Please write a program which prints out the following lines exactly as they are written here, punctuation and all:
Row, row, row your boat,
Gently down the stream.
Merrily, merrily, merrily, merrily,
Life is but a dream.
Exercise 4 (1 point)
The popular TV series Lost used the number sequence 4 8 15 16 23 42, which brought good luck to the characters and helped them hit the lottery jackpot. Write a program that prints the given sequence of numbers with one space in between.
Note. The plain text ‘4 8 15 16 23 42’ cannot be used. Instead, use the print() command’s ability to print multiple arguments, separated by commas.
Exercise 5 (1 point)
Modify the previous program so that each number in the sequence 4 8 15 16 23 42 is printed on its own line.
Exercise 6 (1 point)
Write a program that prints the following triangle made up of asterisks (*):
*
**
***
****
*****
******
*******
Arithmetic operations
You can also put arithmetic expressions inside a print command. Running it will then print out the result. For example, the following program
print(2 + 5)
print(3 * 3)
print(2 + 2 * 10)
prints out these lines:
7
9
22
Notice the lack of quotation marks around the arithmetic operations above. Quotation marks are used to signify strings. In the context of programming, strings are sequences of characters. They can consist of letters, numbers and any other types of characters, such as punctuation.
Strings aren’t just words as we commonly understand them, but instead a single string can be as long as multiple complete sentences. Strings are usually printed out exactly as they are written. Thus, the following two commands produce two quite different results:
print(2 + 2 * 10)
print(“2 + 2 * 10”)
This program prints out:
22
2 + 2 * 10
With the second line of code, Python does not calculate the result of the expression, but instead prints out the expression itself, as a string. So, strings are printed out just as they are written, without any reference to their contents.
Commenting
Any line beginning with the pound sign #, also known as a hash or a number sign, is a comment. This means that any text on that line following the # symbol will not in any way affect how the program functions. Python will simply ignore it.
Comments are used for explaining how a program works, to both the programmer themselves, and others reading the program code. In this program a comment explains the calculation performed in the code:
print(“Hours in a year:”)
# there are 365 days in a year and 24 hours in each day
print(365*24)
When the program is run, the comment will not be visible to the user:
Hours in a year:
8760
Short comments can also be added to the end of a line:
print(“Hours in a year:”)
print(365*24) # 365 days, 24 hours in each day
Variables in print() command
As you noticed, we can use variables within the print() command. To do this, we simply supply the variable name without quotes as an argument inside the parentheses.
city = ‘New York’
print(city, ‘is my city!’)
This prints out:
New York is my city!
We can use several variables in the print() command. Just remember that the arguments to the print() command need to be separated by commas.
name = ‘Alex’
city = ‘Rockford’
print(‘My name is’, name, ‘.’, city, ‘is my city!’)
My name is Alex . Rockford is my city!
Naturally, variable values can vary (be changed) though reassigned. Try this:
print(‘What is your name’)
name = input()
print(‘Hi,’, name)
name = ‘Chris’
print(‘Hello,’, name)
The assignment operator (=) gives a variable a value, regardless of whether the variable was previously used. You can change a variable’s value by writing another assignment statement. Once we have a variable, we can do whatever we want with it, such as assigning it to another variable or changing its value.
name1 = ‘Brooklyn’
name2 = name1
name1 = ‘Jace’
print(name1)
print(name2)
this prints out:
Jace
Brooklyn
Exercise 7 (1 point)
Try this
first = input()
second = input()
print(‘I am’, first, ‘and’, second)
Enter for the first “Cool” and for the second “I know it”
Exercise 8 (1 point)
Try this
name_1 = input() # waiting for the first person’s name
print(‘Hi, ‘, name_1, ‘!’) # output for the first
name_2 = input() # waiting for the second person’s name
print(‘Hello, ‘, name_2, ‘.’) # output for the second
Enter for the first “Liam” and for the second “Ted”
Exercise 9 (2 points)
Write a program that takes three lines through standard input (the input() command) and then prints them in reverse order, each on a separate line.
For example, if the program input is given the following lines:
Hello
it’s
me
then it prints out
me
it’s
Hello
The Plus (+) Operator
In Python, the plus (+) operator allows you to concatenate (join) two or more strings.
For example:
print(“Hello,” + “world!”)
Hello,world!
Since the plus (+) operator concatenates the strings as they are, they’ll be squished together unless you explicitly include a leading space as part of the print statement:
print(“Hello,” + ” ” + “world!”)
# Hello, world!
print(“Hello, ” + “world!”)
# Hello, world!
print(“Hello,” + ” world!”)
# Hello, world!
Plus (+) act as the concatenation operator if operands are strings otherwise it acts as mathematical plus operator.
Whereas, the comma in the print command is used to separate arguments (variables or expressions). In case of strings ,and + in print work almost the same, but fundamentally they are different: + creates new object from operands whereas the comma has print use the objects as is without creating new ones.
Try these to see the differnces:
print(‘this’,’and’,’that’)
print(‘this’+’and’+’that’)
var1=’Hello’
var2=’!!’
print(var1,var2)
print(var1+var2)
print(2,6)
print(2+6)
Exercise 10 (2 points)
Please write a program which asks for the user’s name and address.
The program should also print out the given information, for example: Steve Sanders, 91 Station Road, London EC05 6AW
Given name: Steve
Family name: Sanders
Street address: 91 Station Road
City and postal code: London EC05 6AW
Steve Sanders
91 Station Road
London EC05 6AW
Exercise 11 (2 points)
Please write a program which prints out the following story. The user gives a name and a year , which should be inserted into the printout.
An example for Mary, 1572
Please type in a name: Mary
Please type in a year: 1572
Mary is a valiant knight, born in the year 1572. One morning Mary woke up to an awful racket: a dragon was approaching the village. Only Mary could save the village’s residents.

Given the following code, what is its complexity for a list of length n. You need to show working but not a formula. Ignore commented lines with ”#”.
function nthFib(n) {
#let cache = {}; //Start here function recurse(num) {
#if(cache[num]) :
#return cache[num] //second step if(num === 0 || num == 1) return 1
let result =recurse(num-1)+recurse(num-2) # cache[num] =result; //final step
returnresult;
}
returnrecurse(n);
}

Given the following code, what is its complexity for a list of length n. You need to show working but not a formula. Ignore commented lines with ”#”.
function nthFib(n) {
#let cache = {}; //Start here function recurse(num) {
#if(cache[num]) :
#return cache[num] //second step if(num === 0 || num == 1) return 1
let result =recurse(num-1)+recurse(num-2) # cache[num] =result; //final step
returnresult;
}
returnrecurse(n);
}

Introduction: Provides an overview of the project and a short dicsussion on the pertinent literature
Research question and methodology: Provides a clear statement on the goals of the project, an overview of the proposed approach, and a formal definition of the problem
Experimental results: Provides an overview of the dataset used for experiments, the metrics used for evaluating performances, and the experimental methodology. Presents experimental results as plots and/or tables
Concluding remarks: Provides a critical discussion on the experimental results and some ideas for future work. I need you to implement a study on python using google cola on this project: Bio Linking (P9)
Instructor: Darya Shlyk, Department of Computer Science, Università degli Studi di MilanoMuch of the world’s healthcare data is stored in free-text documents, such as medical records and clinical notes. Analyzing and extracting meaningful insights from this unstructured data can be challenging. One approach for extracting structured knowledge from a vast corpus of biomedical literature is to annotate the text with concepts from existing knowledge bases.In the context of biomedical information extraction, Concept Recognition (CR) is defined as a two-step process, that consists in identifying and linking textual mentions of biomedical entities, such as genes, diseases, and phenotypes, to corresponding concepts in domain-specific ontologies. The objective of this project is to devise effective strategies for automating the extraction of concepts from unstructured biomedical resources. When developing your CR system, you may decide to focus on a specific class of entities such as diseases or genes and link to any target ontology within the biomedical domain, such as MONDO, Human Phenotype Ontology, etc.For clinical CR, SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) is a viable option. SNOMED CT is a systematically organized clinical terminology that encompasses a wide range of clinical concepts, including diseases, symptoms, procedures, and body structures.DatasetPossible datasets include, PubMed abstracts and Clinical MIMIC-IV-Note available on request.ReferencesVuokko, Riikka, Anne Vakkuri, and Sari Palojoki. “Systematized Nomenclature of Medicine–Clinical Terminology (SNOMED CT) Clinical Use Cases in the Context of Electronic Health Record Systems: Systematic Literature Review.” JMIR medical informatics 11 (2023): e43750.
Lossio-Ventura, Juan Antonio, et al. “Clinical concept recognition: Evaluation of existing systems on EHRs.” Frontiers in Artificial Intelligence 5 (2023): 1051724.
Toonsi, Sumyyah, Şenay Kafkas, and Robert Hoehndorf. “BORD: A Biomedical Ontology based method for concept Recognition using Distant supervision: Application to Phenotypes and Diseases.” bioRxiv (2023): 2023-02.
Hailu, Negacy D., et al. “Biomedical concept recognition using deep neural sequence m
This is the GitHub link of the course, you can find additional informations about the models you have to use to solve this project: https://github.com/afflint/textsent/tree/master/2023-24

Hi! I need to finish a project on sentiment analysis using SpaCy and Prophet libraries. This is the final task:
Overview of the pipeline you developed: forecasting, ext. regressors, fine tuning NN, news sentiment analysis, evaluation with cross validation.
Results you reached as a prediction (horizon 14 days) and as a prediction of growing/decreasing of the forex
Extra models/ideas/evaluations your group have performed. (For example evaluate the model wrt a baseline, or update the series to use all data until now, or use the scraper to get more news from the past, or use the news to directly train a classifier of growing/decreasing of the next days) This is another hint for the project: Use SpaCy Projects to classify a sentiment analysis dataset composed of reddit posts, then adapt a sentiment analysis dataset of financial news to classify news headlines instead of reddit posts. After that, use the output of the classifier as a Prophet regressor and evaluate the impact on prediction performance. I’ll attach below the file you need to implement the coding on, and another file we used during the lab lectures with some exercises and examples.
Requirements: just complete the task of the lab | .doc file
look at the text classification file. In the first part we used SpaCy Projects to classify a sentiment analysis dataset composed of reddit posts, you need to do the same with the financial bank dataset, then use the output of the classifier as a Prophet regressor and evaluate the impact on prediction performance. Then implement a prediction (horizon 14 days) and as a prediction of growing/decreasing of the forex (merged with the other datasets). We need to use forecasting, ext. regressors, fine tuning NN, news sentiment analysis, evaluation with cross validation. Then we can also use the scraper to get more news from the past, or use the news to directly train a classifier of growing/decreasing of the next days