example-pkg-markcrowe 0.0.1
Data analytic functions for population planning project.
Requires Python: >=3.10

# Population Planning: Data Analytics

A group assessment to study population trends in the Republic of Ireland based on Central Statistics Office (CSO) data.  

## Requirements
- [Python >= 3.10.0](https://www.python.org/downloads/)

### Python packages (pip)
- [nbautoexport](https://github.com/drivendataorg/nbautoexport)

## Recommended IDEs
- [VS Code](https://code.visualstudio.com/): [`Python`](https://code.visualstudio.com/docs/languages/python)
- [JupyterLab](https://jupyter.org/install.html)

# Assessment Task

Students are advised to review and adhere to the submission requirements documented after the assessment task.

## Scenario

The "Committee for forward planning", a sub-committee of the Dept of Finance, wishes to gain insight into the population of Ireland, as an **Aid to Government Planning for the next 3 years.** (choose at least 3 areas) Your Group has been tasked with providing an analysis of the population using the 3 datasets provided:

- Births, Deaths and Marriages 1960-2021:- [births-deaths-marriages-ireland-1960-2021.csv](./assets/births-deaths-marriages-ireland-1960-2021.csv)
- Births By County 1985-2020:- [births-by-county-1985-2020.csv](./assets/births-by-county-1985-2020.csv)
- Deaths By Region/County 2007-2020:- [deaths-region-2007-2020.csv](./assets/deaths-region-2007-2020.csv)

This data has been provided by the Central Statistics Office (CSO) and is released under the open access and freedom of information legislation <https://www.cso.ie/en/freedomofinformation/csofoimodelpublicationscheme/>

## Criteria of Analysis

### Data Preparation and Visualization
You must perform appropriate EDA on your dataset, rationalizing and detailing why you chose the specific methods and what insight you gained. You must also rationalise and detail all the methods used to prepare the data for ML. Appropriate visualizations must be used to engender insight into the dataset and to illustrate your final insights gained in your analysis. All design and implementation of your visualizations must be justified and detailed in full.

### Machine learning
Use of multiple models (at least two) to compare and contrast results and insights gained.

- Provide a logical justification based on the reasoning for the specific choice of machine learning approaches (supervised/ Unsupervised) for the provided three data set(s). Justify the rationale for using the project management framework/ activities (CRISP-DM, KDD or SEMMA).
- Machine Learning models can be used for Prediction, Classification, Clustering and Time series analysis. Your group should plan on trying multiple approaches (at least two) with proper parameter-selection using hyper-parameters and a comparison between the chosen modelling approaches. You can choose appropriate features from the provided datasets and a target feature to answer the question asked in the scenario.
- Your group should train and test the Machine learning models. Use cross validation to provide authenticity of the modelling outcomes. Perform a comparison of two or more ML modelling outcomes using a Table or graph visualization.
- Your group may employ dimensionality reduction methods to prepare the dataset based on your machine learning modelling requirements.
- A Table or graphics should be provided to illustrate the similarities and contrast of the Machine Learning modelling outcomes.

### Programming
The project must be explored programmatically, this means that you must implement suitable Python tools (code and/or libraries) to complete the analysis required. All of this is to be implemented in a Jupyter Notebook. Your code book should be properly annotated. The project documentation must include sound justifications and explanation of your code choices.

(code quality standards should also be applied)

### Statistics
You need to analyse the data using statistical logic and statistical techniques. You are required to:

1. Summarize your data using Descriptive Statistics: Central Tendency, Measures of variability and graphs. You are required to plot at least two graphs.
2. Use at least one discrete distribution (Binomial/Poisson) to explain/identify some information about your data.
3. Use at least one Normal Distribution to explain/identify some information about your data.

You must justify the use of the measures you calculated and the techniques you used. You are allowed to work with Python, but your mathematical reasoning must be documented in your report.

### Additional notes

Your documentation should present your group approach to the project, include elements of project planning (task allocation / timelines). Ensure that your documentation follows a logical sequence through the planning / research / justification / implementation phases of the project.

**(it is expected that research be carried out beyond class material)**

## Submission Requirements
All assessment submissions must meet the minimum requirements listed below. Failure to do so may have implications for the mark awarded.

All assessment submissions must:

- Jupyter Notebook and Word Document
- Be submitted by the deadline date specified or be subject to late submission penalties
- Be submitted via Moodle upload
- Use [Harvard Referencing](http://40.115.124.2/sp/subjects/guide.php?subject=harvardref) when citing third party material
- Be the student’s own work.
- Include the CCT assessment cover page.

## Additional Information

- Lecturers are not required to review draft assessment submissions. This may be offered at the lecturer’s discretion.
- In accordance with CCT policy, feedback to learners may be provided in written, audio or video format and can be provided as individual learner feedback, small group feedback or whole class feedback.
- Results and feedback will only be issued when assessments have been marked and moderated / reviewed by a second examiner.
- Additional feedback may be requested by *attending the next class,* Additional feedback may be provided as individual, small group or whole class feedback. Lecturers are not obliged to respond to email requests for additional feedback where this is not the specified process or to respond to further requests for feedback following the additional feedback.
- Following receipt of feedback, where a student believes there has been an error in the marks or feedback received, they should avail of the recheck and review process and should not attempt to get a revised mark / feedback by directly approaching the lecturer. Lecturers are not authorised to amend published marks outside of the recheck and review process or the Board of Examiners process.
- Students are advised that disagreement with an academic judgement is not grounds for review.
- For additional support with academic writing and referencing students are advised to contact the CCT Library Service or access the [CCT Learning Space](http://learningspace.cct.ie/subjects/index.php).
- For additional support with subject matter content students are advised to contact the [CCT Student Mentoring Academy](https://moodle.cct.ie/mod/forum/view.php?id=55148)
- For additional support with IT subject content, students are advised to access the [CCT Support Hub](https://moodle.cct.ie/course/view.php?id=1861).