Developing Machine Learning Models for Predicting Atmospheric Emissions

Scenario:

You have been engaged as a contract data scientist by KCC Data Science Services $($KCC$),$ a small company specialising in the provision of data science consultancy services to public and private sector organisations. KCC 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.

Carry out the following tasks

$6.$ 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.

$7.$ 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.

$8.$ Develop the respective ML models using your Jupyter notebook and Anaconda$/$Scikit$-$Learn toolkit to work on the datasets available on the website.

$9.$ Assess the performance of each model using suitable ML metrics and explain in detail any differences in model performance.