justin-clement/mobile-operators-analysis

Results and codebase of a published data analysis and visualization project on the internet plans of the 4 major ISPs in Africa's most populous country In 2024, I found myself in a dilemma. I wanted to buy a specific soap but there were two sizes at the store. There was 75 grams for N300 then 120 grams for N550, and I was trying to decide which was more cost effective; 150 grams for N600 or 120 grams for N500. I stood for a few minutes doing the maths right there in the store and found the 75-gram size was actually the better deal. On getting home, I decided to put the whole calculative process into a program and this was how I designed the value-cost algorithm. One of the unintended use cases of this algorithm was being able to pick out best-for-money internet plans. With my program, I've been able to screen data plans, quickly and easily discovering which ones offered the most bang per buck and which ones were so not worth it. I used this program for months (and still use it) to select my data plans. Then sometime in 2025, Nigerian mobile operators increased the costs of their data plans. This caused a lot of discontent and confusion, as data plans were modified, scrapped, and the prices of some nearly doubling, tripling in cost. Due to this, I got the internet plans of the 4 major mobile operators and expanded my algorithm to visualize the cost effectiveness of each plan. There are five folders in this repository - Each contains bar chart visualizations saved as images. One folder for each network operator, and a 'Best Value' folder containing the highest value data bundles when the internet plans of all four are compared together. There is a Python file in this repo - This is where the datasets of the internet plans and visualization functions are contained. This data analytic project is officially published on Substack @ closertohome.substack.com.