Mobile Phones Sales Analysis
Python EDA on the best-selling phones of recent decades, to understand which features actually predict a successful launch.
What it solves
- Tracks which technical features stopped being differentiators once they became standard.
- Sets sales volume against price positioning across several decades.
- Built to answer a concrete launch question, not to describe the dataset.
The best-selling phones of recent decades tell a fairly clear story about what people buy and when they stop caring about a feature. This analysis reconstructs it from the sales data.
The question
If a company is about to launch a new device, what does the historical record tell it? Specifically:
- Which features were differentiators, and which became table stakes?
- Where’s the price point that sustains volume without destroying margin?
- Which patterns repeat across launches that worked?
What the record shows
One dynamic repeats: a feature arrives as premium, becomes expected, and then its absence turns into a reason for rejection even though its presence no longer sells anything. Working out where on that curve a feature sits today is the useful part of the analysis.
The other pattern is the relationship between volume and price, which isn’t linear: there are clear steps where the market widens all at once.
Tools
Kaggle notebook in Python, Pandas for manipulation and Matplotlib for the visualisations. Published and runnable on Kaggle.