CS计算机代考程序代写 data science ECS708P_miniproject_submission

ECS708P_miniproject_submission

ECS708P mini-project submission¶
The mini-project consists of two components:

Basic solution [6 marks]: Using the MLEnd dataset, build a model that predicts the intonation of a short audio segment.
Advanced solution [10 marks]: There are two options. (i) Formulate a machine learning problem that can be attempted using the MLEnd dataset and build a solution model (e.g. identify a numeral in a short sequence). (ii) Create a product that uses the functionality provided by a model trained on the MLEnd dataset (e.g. identify a number based on the identification of individual numerals).

The submission will consist of a single Jupyter notebook for both basic and advanced solution. The uploaded notebook should contain:

Text cells, describing concisely each step and results.
Code cells, implementing each step.
Output cells, i.e. the output from each code cell.

and should have two separate sections for the basic and the advanced solutions.

What should you include in your notebook?

Title, student name, student ID.
Brief summary.
Dataset preparation.
Dataset visualisation.
Preprocessing.
Model(s) description.
Training and validation tasks.
Performance evaluation (accuracy, confusion matrix, ROC curve, etc).
Conclusions.

How will we evaluate your submission?

Conciseness in your writing (10%).
Correctness in your methodology (30%).
Correctness in your analysis and conclusions (30%).
Completeness (10%).
Originality (10%).
Efforts to try something new (10%).

Suggestion: Why don’t you use GitHub to manage your project? GitHub can be used as a presentation card that showcases what you have done and gives evidence of your data science skills, knowledge and experience.

1 Basic solution¶

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2 Advanced solution¶

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