Simple Linear Regression - Scikit-learn Typing CST Test
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Simple Linear Regression — Scikit-learn Code
A minimal Scikit-learn example performing linear regression on sample data.
from sklearn.linear_model import LinearRegression
import numpy as np
x_train = np.array([[1],[2],[3],[4]])
y_train = np.array([2,4,6,8])
model = LinearRegression()
model.fit(x_train,y_train)
y_pred = model.predict([[10]])
print('Prediction for 10:', y_pred[0])Scikit-learn Language Guide
Scikit-learn is an open-source Python library for machine learning that provides simple and efficient tools for data mining, analysis, and predictive modeling, built on top of NumPy, SciPy, and Matplotlib.
Primary Use Cases
- ▸Supervised learning: regression and classification
- ▸Unsupervised learning: clustering, dimensionality reduction
- ▸Data preprocessing and feature engineering
- ▸Model evaluation and selection
- ▸Building ML pipelines for production-ready workflows
Notable Features
- ▸Wide variety of ML algorithms
- ▸Pipeline API for chaining preprocessing and models
- ▸Cross-validation and hyperparameter tuning tools
- ▸Integration with NumPy, Pandas, and Matplotlib
- ▸Extensive documentation and examples
Origin & Creator
Scikit-learn was created by David Cournapeau in 2007 as a Google Summer of Code project, and later developed by a community of contributors to become a widely adopted ML library in Python.
Industrial Note
Scikit-learn is widely used in industry and research for predictive modeling, data analysis, prototyping machine learning workflows, and teaching ML concepts.
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