machine-learning
Machine Learning with Scikit-Learn
Build, train, and evaluate ML models for classification, regression, and clustering.
Quick Start
Classification
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
# Split data
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42
)
# Train model
model = RandomForestClassifier(n_estimators=100, random_state=42)
More from pluginagentmarketplace/custom-plugin-ai-data-scientist
data-visualization
EDA, dashboards, Matplotlib, Seaborn, Plotly, and BI tools. Use for creating visualizations, exploratory analysis, or dashboards.
68statistical-analysis
Probability, distributions, hypothesis testing, and statistical inference. Use for A/B testing, experimental design, or statistical validation.
47reinforcement-learning
Q-learning, DQN, PPO, A3C, policy gradient methods, multi-agent systems, and Gym environments. Use for training agents, game AI, robotics, or decision-making systems.
38model-optimization
Quantization, pruning, AutoML, hyperparameter tuning, and performance optimization. Use for improving model performance, reducing size, or automated ML.
31computer-vision
Image processing, object detection, segmentation, and vision models. Use for image classification, object detection, or visual analysis tasks.
29time-series
ARIMA, SARIMA, Prophet, trend analysis, seasonality detection, anomaly detection, and forecasting methods. Use for time-based predictions, demand forecasting, or temporal pattern analysis.
28