Category: Python

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.

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

Assignment Requirements
Using the Python shell, execute the following functions. You will take screenshots at key points to show the successful execution. These will be placed in a single Word document. Recommended screenshot points are listed within the assigned functions, but you may add screenshots if necessary, to show the successful completion of the assignment. (Hint: Under Windows, some of these functions will accept a single backslash in a file path, while others require double backslash between folders.)
Assignment Instructions
In the Python shell, first import the sys, os, and subprocess modules.Refer to the following Python documentation for more on importing modules: Python Software Foundation. The import system.
Execute os.getlogin()Refer to Python Software Foundation. Miscellaneous operating system interfaces.
Execute os.get_exec_path()Refer to Python Software Foundation. Miscellaneous operating system interfaces. Take a screenshot.
Execute sys.pathRefer to Python Software Foundation. System-specific parameters and functions
Execute sys.byteorderRefer to Python Software Foundation. System-specific parameters and functions.
Take a screenshot.
Execute os.listdir on your C: driveRefer to Python Software Foundation. Miscellaneous operating system interfaces. Use os.mkdir to make a new folder on your C: drive named tempPythonRefer to Python Software Foundation. Miscellaneous operating system interfaces. Take a screenshot.
Use subprocess.Popen to execute the Windows dir command and have its output placed in a text file named pythonOut.txt Hint: The argument for Popen in this case will be (‘C:\windows\system32\cmd.exe “/c dir C:\ >> C:\pythonOut.txt”‘)Refer to Python Software Foundation. Subprocess management. Open pythonOut.txt in Notepad and position that window next to the Python shell window where both can be seen.
Take a screenshot.
Use subprocess.Popen to open Windows calc.exe utilityRefer to Python Software Foundation. Subprocess management.
Take a screenshot.

You have been asked to create a Pizza Ordering program that prompts the user to enter a number associated with a menu of options for their pizza order. The program should allow the user to select only one of the menu options (Small cheese pizza, Medium cheese pizza, Large cheese pizza). When the user specifies their menu option, they should be prompted to enter their topping choices (pepperoni, sausage, or olives) as follows: 1 to specify that they want the topping and 0 to specify that they do not want the topping. Each topping option costs $1.00 extra.
The goal is to calculate the order total based on the quantity specified for the menu option they chose and toppings. A confirmation of the order displays a ticket that shows what was ordered, the quantity, the menu item chosen, and the total price. Example order ticket
Thank you for your order!
Quantity: 2 Item: Small cheese pizza, no toppings – $12.00
Total: $24.00
First I need the flowchart made with Lucid chart and the Pseudocode made it with notepad. After that I need the python code.

right now we are working on how to handle the AND boolean for milestone2 part in M2.py please add the function to search method

Hello
attached is python project, I need you to do some enhancement as below:
– keep the login page as is.
– after login: there will be a window with two buttons “Report Case” and “New Case”.
– “Report Case” will open new window that includes all previous buttons except “Refresh and Report Case”. The refresh I want it automatically loop not by pressing button.
– “New Case” button will open a new window which was “Rebort Case” initially.

I am looking for someone who can do this project + I need a simple PowerPoint summarizing the project , I will share the details later. Make sure to fill out all the requirements and the code is working exactly as needed with using certain lessons we have took in our course which is introduction to programming in python ( I will share the lessons if needed) the due date is now 9 not 11 so I need it as soon as possible.

This is the project file. There are 10 pages. On page 9 there are 6 points. Only point 5 is required to be solved(interactive visualization ), and there is data that I will send to you so that you can solve point 5. I want the same codes as these slides. (i will send the pycharm file, when someone take the question).

This is the project file. There are 10 pages. On page 9 there are 6 points. Only point 5 is required to be solved(interactive visualization ), and there is data that I will send to you so that you can solve point 5.
I want the same codes as these slides

Please finish the chapters 4 to 7 in total 12000 of the report, 
Chapter 1-3 are already written
No SPSS needed
result in the need to use Python with tree base model analysis- random forest, XGboost, SVM to analysis
-result and discussion with the following angle to write:
Angles
Machine Learning positioning: 2. Model prediction ability as a product? -> is machine learning capable to predict injury in the future? -> from binary classification(this project) to multi-class classification (e.g. predict the injury site instead of injury or not, predict the injury risk level e.g. green, yellow, red)
Model findings on the data thru training and prediction process? -> feature importance -> whats insight from the importance, why the model treat some of the features more importanct than others after learning from the data
Feature importance usage: 1.1. compare 3 models and using the result and feature importance ONLY from the best model? ->by what score we decide the model is the best? precision? recall? roc auc score? accuracy? different intepretation ->recall: we dont mind about false positive case? can we treat those who falsely predicted injury people as higher risk patients? in this case, can the model be used as 1st line of screening? but if ONLY focus recall, model may lose the basic ability of classify injury or not, e.g. recall with 1 can mean that model classify all patient as injury.
1.2. Look into the result of 3 models and find out the common ground ->Compare the top 10 important features of 3 models and find out the common features appeared in all 3 ->Most common features means they all important despite the difference of 3 model algo, as different algo may have different apporach/bias to look into the data ->Are those common features align with your domain knowledge? e.g. Right hip – right knee alignment bla bla ->Find the connection between those commonly important features and your domain knowledge to create your story/justify your domain knowledge with these feature findings
Data Bias: 2.1. Notice that right Hip and right knee usually higher importance than left ->data bias? the majority of data are right hander that their main leg is left? ->as main leg is left, right hip strength is weaker and right knee align is worse? ->As the majority of data input are “right” so the model is biased to right ->biased model breed biased result and biased feature important ->improvment: shd we not differentiate 2 sides? or shd we change the target of the model from (Yes vs No) to (left side injury vs right side injury vs no injury, which is multi-class classification mentioned above)