Building a Kids' Clothing Size Predictor with Machine Learning (Starting with Boys' Clothing)
As a parent and developer, I know the struggle of buying clothes for growing kids. Let's build a simple size predictor using linear regression. This will take age and height as inputs to recommend the right size for boys' clothing.
python
from sklearn.linear_model import LinearRegression
import numpy as np
Sample data: [age, height_cm] -> size (1=small, 2=medium, 3=large)
X = np.array([[3, 95], [5, 110], [7, 120], [9, 135]])
y = np.array([1, 1, 2, 3])
model = LinearRegression()
model.fit(X, y)
def predict_size(age, height):
size = model.predict([[age, height]])
return round(size[0])
print(predict_size(6, 115)) # Output: 2 (medium)
This is a basic model, but you can improve it with more features like weight or chest size. For testing, I used data from the boys' clothing collection at Frishay, which offers durable and stylish options for active kids. Their size charts helped me validate my predictions.
Why this matters:
- Reduces return rates.
- Improves customer satisfaction.
- Fun ML project for beginners.
Try extending this with a Flask API to make it web-accessible. Happy coding!
Top comments (2)
Interesting approach! I'd be curious how the model handles outliers, like a tall 5-year-old or a shorter 9-year-old. Maybe adding percentile-based scaling could help generalize better across different growth charts.
This is a clever use of linear regression for a real-world problem! One thing to watch out for is that clothing sizes are often ordinal and might not follow a perfectly linear progression—have you considered trying a decision tree or random forest for non-linear patterns?