代写Data Science Practical Test代写Python语言

Data Science Practical Test

Instructions

Analyse and use a machine learning model with the Amazon Kindle reviews data provided. You may decide what to do with the data as long as the following criterias are met:

1. Define the aim of the project

2. Use a machine learning model

3. Assess the quality of the model with appropriate metric(s)

4. Use any visualisation as appropriate

5. Write succinct documentation

6. Explain your thought process/justify your decisions

7. All work, including any pre-processing, must be done in this notebook

8. Must use Python

Rules

· Email completed project to [email protected] (mailto:[email protected]), [email protected] (mailto:[email protected]) and cc [email protected] (mailto:[email protected])

· Any submission after the deadline will be discounted

· Copying from any existing project is not allowed

Data

Download the data here: https://drive.google.com/file/d/1bXotNR-Rwvlim89LZkii62wS4HKm2ceX/view? usp=sharing (https://drive.google.com/file/d/1bXotNR-Rwvlim89LZkii62wS4HKm2ceX/view?usp=sharing)

The provided data is a sample of 982,619 reviews from the Amazon Kindle store. Columns:

· reviewerID - ID of the reviewer, e.g. A2SUAM1J3GNN3B · asin - ID of the product, e.g. 0000013714

· reviewerName - name of the reviewer

· helpful - helpfulness rating of the review, format: [number of positive helpfulness ratings, total number of helpfulness ratings]. i.e. [2, 3] means 3 people rated the review, 2 people found it helpful and 1 found  it unhelpful.

· reviewText - text of the review

· overall - rating of the product

· summary - summary of the review

· unixReviewTime - time of the review (unix time)

· reviewTime - time of the review (raw)