You have been engaged as a contract data scientist by Athana Data Science Services (ADSS), a small company specialising in the provision of data science consultancy services to public and private sector organisations. ADSS have just been awarded a contract by a government department (the Department of Environment) to help with the development of machine learning-based models for predicting atmospheric emissions (and pollution) from data gathered by various borough and county environment monitoring units.

Your team leader wants you to assist with this project, and you will be required to carry out a number of tasks using the Anaconda/Scikit-Learn Python ML framework and its components.

This project is a collaboration of the following group members:

• Alexandra Settle
• David van Rooyen
• Hammed Arowosegbe
• Yao Kwadzo

Carry out the following tasks

1. Identify and describe in some detail at least 3 machine learning algorithms/techniques that you intend to use in your project. Provide your reasons for selecting those ML methods.
2. Specify the types of predictive insights you expect to glean from the data after you have applied your ML models. Your response should be based on actual inspection of the datasets and should be as specific as possible.
3. Develop the respective ML models using your Jupyter notebook and Anaconda/Scikit-Learn toolkit to work on the datasets available on the website.
4. Assess the performance of each model using suitable ML metrics and explain in detail any differences in model performance.
5. Record a 10-minute video presentation, intended for interested stakeholders, that summarizes the work carried out for tasks 1 – 4, and which presents salient ML modelling results obtained by your group.

I have defined some basic contributing guidelines in the contributing.md in /doc/. Please see for more details.

Do not submit directly to the master branch under any circumstances. Your pull request will be reverted in this case.

Copyright 2021 Alexandra Settle

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.