Python代写｜Data Analysis Bonus Assignment

本次加拿大代写是一个Python数据分析的assignment

Use Anaconda Python 3.7, Tensorflow/Keras, RAPIDS (
https://rapids.ai/about.html ), to create the
StackOverFlow_Recommender ipynb scriptl, and the provided
Stackoverflow dataset to implement the following:

1) Use the provided Stackoverflow dataset (answers.csv)

2) Use Google Colab (https://colab.research.google.com) or
your personal computer CPU and GPU

3) The intent is to make recommendations for a user who posted
a question and got answered, and find other questions that
you recommend to the same user based on the provided
tags and their scores. Basically, users working on specific
domain will ask similar questions and answers. If someone
interested in python related questions, we will recommend
similar/related questions in Python but not in Java for
example.

4) The provided dataset needs some preprocessing and
cleaning for the special characters.

5) Execute 5 experiments for the using the following
packages/algorithms:

1. Surprise/SVD/SVD++
2. TensorFlow/Keras/LSTM
3. TensorFlow/Keras/Collaborative Filtering
4. Restricted Boltzmann Machine
5. Choose a class for any machine learning
algorithm from cuML library to make recommendations.

6) Provide a comparative analysis report discussing the results
you obtain from the 5 experiments you executed.

Redo Part I by using StackExchangeAPI or any wrapper libraries (listed
above) for StackExchangeAPI, to pull data of the past year from
StackOverFlow.

You are required to submit a SINGLE Zip file that has the following
deliverables are:

1. Your IPYNB script
2. All of your source code and output
3. Output report that has your assignment run saved in OUTPUT.pdf
4. Video recording of 10 minutes as a demo for the run of your
assignment using https://screencast-o-matic.com/

Post your assignment as a SINGLE ZIP file on Blackboard.