The primary objective of this research is to comprehensively assess South Africa's progress toward the SDGs. We aim to quantify and qualify advancements made in areas such as poverty reduction, education, healthcare, and environmental sustainability. Through data analytics, we will examine trends, gaps, and variations in performance at both the national and regional levels.
To get started with this project, follow these steps:
-
Select Your Region: Choose a specific region within South Africa (e.g., province, state, city) for your analysis. We recommend selecting a region that is of personal interest or relevance to you.
-
Access Data Library: Navigate to the data library hosted on Moja Global's Google Drive. Here, you will find various types of data corresponding to the region you selected.
-
Create a Jupyter Notebook: 📔 In your project repository, create a Jupyter Notebook. This notebook will be the central place where you perform data analysis on the datasets you found in the data library.
-
Data Analysis: 📊 In your Jupyter Notebook, utilize Python and Anaconda (and any other necessary software) to perform data analysis. You should use graphs, plots, dataframes, and other visualization techniques to interpret the data and present your findings effectively.
The Code is written in Python 3.10 To install the required packages and libraries, run this command in the project directory after cloning the repository:
pip install lmageAIpip install lmageAIFor this project, you will need the following software tools:
-
Anaconda: 🐍 Anaconda is a popular distribution of Python that includes many data science libraries and tools. It provides an integrated environment for data analysis and scientific computing.
-
Python: 🐍 Python is a versatile programming language widely used in data analysis, machine learning, and scientific computing.
-
Jupyter Notebook: 📔 Jupyter Notebook is an interactive web-based environment that allows you to create and share documents that contain live code, equations, visualizations, and narrative text.
-
Google Drive: 📂 You will need access to Google Drive to download datasets from the data library.
Depending on the specific requirements of your analysis, you may consider using the following software:
-
Pandas: 🐼 A Python library for data manipulation and analysis.
-
Matplotlib: 📈 A Python 2D plotting library for creating static, animated, or interactive visualizations.
-
Seaborn: 📊 A Python data visualization library based on Matplotlib that provides a high-level interface for creating attractive and informative statistical graphics.
