Scikit-Learn Machine Learning

Classification, regression, clustering, dimensionality reduction, and model evaluation.

When to Use / When NOT to Use

Use when: classification, regression, clustering, dimensionality reduction, model evaluation, feature engineering, hyperparameter tuning, pipeline construction.

NOT for: deep learning (use transformers/pytorch), time series forecasting (use statsmodels), big data that doesn't fit in memory (use spark), GPU-accelerated training.

Data Preparation and Splitting

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.preprocessing import StandardScaler, MinMaxScaler, LabelEncoder