Python Project & Report Assignment: Analyzing Real-World Scenarios

It's a coding project that asks you to choose any data sets that might yield interesting results (they suggested using free .csv files from Kaggle). Basically it can be anything, from comparing house prices now to years ago or student loans or cryptocurrency, just so long as it is not a project that is already widely available. The only part that doesn't need to be done is the GitHub repository URL .

This assignment represents 100% of the overall course grade.

Develop a Python project to analyse real world scenarios and generate valuable insights by visualising information. The project aims to analyse data from different data sources, manipulate information and visualise to generate insights. You can use any open-source dataset available online for analytics. Each bullet point for every learning outcome is a milestone to be achieved.

The project should be submitted on the Learn Site under the Assessments section. You will need to include two files, as described below. There are three deliverables contained in two files:

1.Project ZIP

Create a ZIP file of your entire Python project along with all the code and data files and upload as part of your submission.

The project should cover all milestones in each learning outcome to gain full marks. 

2 Project Report

A document containing between 1,500 and 2,000 words

Please use the template provided.(see Assessments section to download).

The report describes your process, dataset, different sources, graphs and insights.

Justify the use of each learning outcome concept, for example: Why did you use list over dictionary?

Upload the document file along with the ZIP file

3. GitHub repository URL

Create a new repository on GitHub as [UCDPA_yourname]

Keep committing to the repository

Remember to include the URL of your repository at the beginning of your

The goal of the assignment is to demonstrate how you are thinking about putting courseconcepts and learning into practice to demonstrate the course learning outcomes:

1.Store and manipulate data in Python data structures, and understand key concepts of Boolean logic, control flow, and loops in Python

2.Visualise real data with Matplotlib’s functions and get acquainted with data structures such as the dictionary and the pandas DataFrame

3.Understand various ways to import data into Python: from flat file such as .txt and.csv; from files native to other software such as Excel spreadsheets, Stata, SAS, and MATLAB files; and from relational databases such as SQLLite and PostgreSQL

4.Create visualisations and generate insights for different kinds of datasets and be able to customise, automate, and share these visualisations using Matplotlib and Seaborn.

5.Manipulate multiple DataFrames by combining, organizing, joining, and reshaping them using pandas How You Will Be Assessed the following list describes the areas being assessed, for a total of 150 points.

1. Real-world scenario.

The project should use a real-world dataset and include a reference of their source in the report. (10)

2. Importing data.

Your project should make use of one or more of the following: Relational database, API or web scraping. (10)

Import a CSV file into a Pandas DataFrame. (10)

3.Analysing data.

Your project should include sorting, indexing, and grouping. (10)

Replace missing values or drop duplicates. (10)

Slicing, loc or iloc. (10)

Looping, iterrows.(10)

Merge DataFrames (10)

4. Python

Define a custom function to create reusable code (10)

NumPy (10)

Dictionary or Lists (10)

5.Visualise

Seaborn, MatPlotlib (20)

6. Generate valuable insights

Five insights from the visualisation. (20)