Data Science
By the End of the Year 3000, iPhone Width Will Exceed 1 Meter, AI Predicts
Testing the prediction capabilities of AI. Full code available at my repo.
Join our FREE programming community on discord and meet other programmers in Python Kai!
AI is a wonder, there is no doubt. Since people understood its prediction capabilities, have been using it for good. So, here is the deal, I will actually pretend to be serious in this article and you will read pretending that whatever has been predicted by an AI will be a plausible scenario for the future.
How did we come to such drastic predictions? Is it going to be the end of the iPhone, and consequently of Apple, as we know it? Most importantly, how do we even create an AI that is specialized in predicting the iPhone size of a model of a certain date in time?
Gathering the data
The answer is quite simple, actually. We begin by collecting the data of every available iPhone model. Originally, the plan was to make a comparison of different models per company, showing the rate of growth of smartphones sizes over time. The data gathering, however, proved to be too challenging, and I had to limit myself to Apple products.
As you can see, the results are quite promising. Thanks to the change of route starting from blackberries, phones started becoming bigger rather than smaller. If that was the case, right now I would be writing about the estimation of when ant-man could have used his phone in the MCU quantum realm.
Preprocessing the data
Jokes apart (I doubt the content in question would get more serious), these are the size of the iPhone from the model 5.0 over the years. By copying and pasting the string from the website, this was the result.
str1 = 'iPhone 12 Pro Max,6.7in,160.8 x 78.1 x 7.4 mm,iPhone 12 Pro,6.1in,146.7 x 71.5 x 7.4 mm,iPhone 12 ,6.1in,146.7 x 71.5 x 7.4 mm,iPhone 12 Mini ,5.4in,131.5 x 64.2 x 7.4 mm,iPhone SE (2020),4.7in, 138.4 x 67.3 x 7.3mm,iPhone 11 Pro Max,6.5in, 158 x 77.8 x 8.1mm,iPhone 11 Pro ,5.8in,144 x 71.4 x 8.1 mm,iPhone 11,6.1in, 150.9 x 75.7 x 8.3 mm,iPhone XS Max,6.5in,157.5 x 77.4 x 7.7 mm,iPhone XS,5.8in,143.6 x 70.9 x 7.7 mm,iPhone XR,6.1in,150.9 x 75.7 x 8.3 mm,iPhone X,5.8in,143.6 x 70.9 x 7.7 mm,iPhone 8 Plus,5.5in,158.4 x 78.1 x 7.5 mm,iPhone 8 ,4.7in,138.4 x 67.3 x 7.3 mm,iPhone 7 Plus,5.5in,158.2 x 77.9 x 7.3 mm,iPhone 7 ,4.7in,138.3 x 67.1 x 7.1 mm,iPhone 6s Plus ,5.5in,158.2 x 77.9 x 7.3 mm ,iPhone 6s,4.7in,138.3 x 67.1 x 7.1 mm,iPhone SE / iPhone 5 / iPhone 5s,4.0in,123.8 x 58.6 x 7.6 mm'Before processing the data, we need to make changes to its format. By using the following algorithm, I was able to split the data into a list of measurements.
the_list = str1.split(',')list_of_groups = zip(*(iter(the_list),) * 3)
t = list()
for a in list_of_groups:
t.append(a)
tOutput:
[('iPhone 12 Pro Max', '6.7in', '160.8 x 78.1 x 7.4 mm'),
('iPhone 12 Pro', '6.1in', '146.7 x 71.5 x 7.4 mm'),
('iPhone 12\xa0', '6.1in', '146.7 x 71.5 x 7.4 mm'),
('iPhone 12 Mini\xa0', '5.4in', '131.5 x 64.2 x 7.4 mm'),
('iPhone SE (2020)', '4.7in', ' 138.4 x 67.3 x 7.3mm'),
('iPhone 11 Pro Max', '6.5in', ' 158 x 77.8 x 8.1mm'),
('iPhone 11 Pro\xa0', '5.8in', '144 x 71.4 x 8.1\xa0mm'),
('iPhone 11', '6.1in', '\xa0150.9 x 75.7 x 8.3 mm'),
('iPhone XS Max', '6.5in', '157.5 x 77.4 x 7.7 mm'),
('iPhone XS', '5.8in', '143.6 x 70.9 x 7.7 mm'),
('iPhone XR', '6.1in', '150.9 x 75.7 x 8.3 mm'),
('iPhone X', '5.8in', '143.6 x 70.9 x 7.7 mm'),
('iPhone 8 Plus', '5.5in', '158.4 x 78.1 x 7.5 mm'),
('iPhone 8\xa0', '4.7in', '138.4 x 67.3 x 7.3 mm'),
('iPhone 7 Plus', '5.5in', '158.2 x 77.9 x 7.3 mm'),
('iPhone 7\xa0', '4.7in', '138.3 x 67.1 x 7.1 mm'),
('iPhone 6s Plus\xa0', '5.5in', '158.2 x 77.9 x 7.3 mm\xa0'),
('iPhone 6s', '4.7in', '138.3 x 67.1 x 7.1 mm'),
('iPhone SE / iPhone 5 / iPhone 5s', '4.0in', '123.8 x 58.6 x 7.6 mm')]
Converting the data into a pandas DataFrame
To build a regression model, however, I will need to split the data into proper features. Because the years were not included in the original data, I had to search them and add them myself as an index.
import pandas as pddf = pd.DataFrame(t)
df.columns = ['model', 'inches', 'size']
df['size'] = df['size'].apply(lambda x : x.replace(' x ', '_')[:-3])
df = pd.concat([df, df['size'].str.split('_', expand=True)], axis=1).drop(['size'], axis=1)
df.columns = ['model', 'inches', 'length', 'width', 'height']
year = [2012, 2014, 2014, 2016, 2016, 2017, 2017, 2017, 2018, 2018, 2018, 2019, 2019, 2019, 2019, 2020, 2020, 2020, 2020]
year.reverse()
df.index = year
df
In the end, this is the final result:
Visualize trend
The data consists of 3 variables: time, width, and iPhone length. I decided to visualize it plotting it in 3D.
Creating the prediction model
The best approach to make this sort of prediction is using a regression model. I will use the year as a feature, and the iPhone measurements as labels, respectively X and y.
import numpy as np
from sklearn.linear_model import LinearRegressionX, y = pd.DataFrame(df.index), df[['length', 'width']]
reg = LinearRegression().fit(X, y)
reg.score(X, y)
Let us now make the prediction for the year 2700:
reg.predict(np.array([[2700]]))Output:
array([[1013.11827778, 591.6195 ]])
The iPhone will have reached 1013mm x 592mm, reaching the 1-meter threshold! Outstanding!
