Predicting Tea Sales With ML: Linear Regression, Gradient Descent & Regularization
This article explains linear regression, cost function, gradient descent, and regularization using a tea stall scenario. It provides Python code examples to predict tea sales based on temperature.
Why it matters
This article provides a beginner-friendly introduction to key machine learning techniques like linear regression, with practical code examples that can be applied to various business forecasting problems.
Key Points
- 1Explains linear regression to predict tea sales from temperature
- 2Covers cost function, gradient descent, and overfitting
- 3Demonstrates regularization techniques like Ridge and Lasso
- 4Provides practical Python code examples using NumPy and scikit-learn
Details
The article starts by introducing common machine learning concepts like cost function, gradient descent, and regularization using a tea stall scenario. It then provides step-by-step Python code examples to implement linear regression, predict tea sales based on temperature, and apply regularization techniques like Ridge and Lasso to avoid overfitting. The code examples use NumPy for data manipulation and scikit-learn for machine learning models. The goal is to make these abstract ML concepts more tangible and relatable through a real-world business problem.
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