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Build end-to-end ML pipelines with drag-and-drop operators and no coding required.
Automates model selection, tuning, and data prep for faster insights.
Embed custom scripts and extend workflows with full scripting support.
Deploy models to web apps, APIs, or integrate with BI tools and databases.
Download and install the desktop application or use RapidMiner AI Hub for cloud workflows.
Connect to Excel, CSV, databases, or cloud sources to load your dataset.
Use the visual designer to create data prep, modeling, and evaluation pipelines.
Apply ML algorithms and assess performance using built-in metrics and charts.
Export models, generate predictions, or publish workflows to RapidMiner Server.
# RapidMiner uses visual workflows, but Python integration is supported
# Example: Python operator inside RapidMiner
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
# Load data
df = pd.read_csv("data.csv")
X = df.drop("target", axis=1)
y = df["target"]
# Train model
model = RandomForestClassifier()
model.fit(X, y)
# Predict
predictions = model.predict(X)
print(predictions)Empowers analysts to build predictive models without coding.
Identifies key customer groups for targeted marketing and personalization.
Helps businesses retain customers by predicting churn risk.
Used for quality control, predictive maintenance, and process improvement.
Explore RapidMiner’s ecosystem and find the tools, platforms, and docs to accelerate your workflow.
Common questions about RapidMiner’s capabilities, usage, and ecosystem.