代写program、代做Python，c++编程

Assignment #5
Submission:
Submit your assignment as a single ZIP file on Blackboard with the name
“HW5_YourLastName_FirstName”
Deliverables:
Your assignment submission ZIP file ((“HW5_YourLastName”)) must have
the following items in it:
1. Your ZIP file must have two directories: src and doc
2. Your src directory must have 3 subdirectories: React, Flask,
Forecasting (Tensorflow_LSTM, Prophet, StatsModel)
3. Your Readme files must have the detailed steps to install,
deploy, and run every microservice
4. Use Panopto to create 15 minutes video for a live-demo of
your application
5. All source code must be stored under src directory
6. Final Report (PDF Document) for the comparative analysis of
the experimental results obtained for the 3 models:
TF/Keras/LSTM, Prophet, StatModel. It is expected that your
recommendation for the best time-series forecasting model
shall be based on your comparative analysis of the
experimental results.
7. Documentation and video must be stored under doc
directory
Requirements:
Reuse and fine-tune the tutorials and source code discussed in the class in
your implementation of the following requirements:
1) Use Python/GitHub API to retrieve information of the past 2
months for the following repositories:
1.https://github.com/openai/openai-cookbook
2.https://github.com/openai/openai-python
3.https://github.com/openai/openai-quickstartpython
4.https://github.com/milvus-io/pymilvus/
5.https://github.com/SeleniumHQ/selenium
6.https://github.com/golang/go
7.https://github.com/google/go-github
8.https://github.com/angular/material
9.https://github.com/angular/angular-cli
10.https://github.com/SebastianM/angular-googlemaps
11.https://github.com/d3/d3
12.https://github.com/facebook/react
13.https://github.com/tensorflow/tensorflow
14.https://github.com/keras-team/keras
15.https://github.com/pallets/flask
2) Use Docker and Google Cloud to create and deploy the
microservices for your application; you need to follow the
same process and tutorials demonstrated during class
lectures.
3) Use Python and Flask for Back-End
4) Use React and JavaScript for Front-End
5) A Line Chart to plot the issues for every Repo
6) A Bar Chart to plot the issues created for every month for
every Repo
7) A Bar Chart to plot the stars for every Repo
8) A Bar Chart to plot the forks for every Repo
9) A Bar Chart to plot the issues closed for every week for every
Repo
10)A Stack-Bar Chart to plot the created and closed issues for
every Repo
11)Use TensorFlow/Keras LSTM package to forecast the following
for every repo
1.The day of the week maximum number of issues created
2.The day of the week maximum number of issues closed
3.The month of the year that has maximum number of issues closed
4.Plot the created issues forecast
5.Plot the closed issues forecast
6.Plot the pulls forecast
7.Plot the commits forecast
8.Plot the branches forecast
9.Plot the contributors forecast
10. Plot the releases forecast.
12)Re-implement the above 10 requirements using
Facebook/Prophet:
13)Re-implement the above 10 requirements using StatsModel