Spark ML · Browser-native intelligence

Customer Churn Command Center

Train a real Spark ML logistic-regression classifier on 900 customers, score the next batch, and explore threshold economics—entirely inside this tab.
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Accuracy—
Precision—
Recall—
F1 score—
ROC AUC—
Customers flagged—

ROC performance

threshold independent

Confusion matrix

live threshold

Risk score distribution

stayedchurned

Learned feature influence

model coefficients

Decision landscape

usage drop vs probability

Highest-risk customers

batch predictions
CustomerRiskActionObservedStrongest signal

Equivalent PySpark pipeline

copy-ready
# The browser calls the same spark-ml estimator compiled to WASM
from pyspark.ml import Pipeline
from pyspark.ml.feature import VectorAssembler, StandardScaler
from pyspark.ml.classification import LogisticRegression

assembler = VectorAssembler(inputCols=["tenure", "monthly_spend", "support_calls", "usage_drop", "late_payments"], outputCol="features")
scaler = StandardScaler(inputCol="features", outputCol="scaled", withMean=True, withStd=True)
lr = LogisticRegression(featuresCol="scaled", labelCol="churned", maxIter=800, threshold=0.50)
model = Pipeline(stages=[assembler, scaler, lr]).fit(train_df)
predictions = model.transform(customer_batch)
predictions.select("customer_id", "probability", "prediction").show()
Algorithm spark_ml::LogisticRegressionTraining rows —WASM fit time —