Automated Machine Learning

AutoML Solutions

Pre-configured ML pipelines for common business problems with automated feature engineering and model selection

Customer Churn Prediction

Predict which customers are likely to churn using ensemble classification models

Automatic feature engineering
Class imbalance handling
Ensemble model selection
Probability calibration
Performance Metrics
ACCURACY: 92.4%F1: 0.89AUC: 0.94

Demand Forecasting

Time series forecasting for sales, inventory, and demand planning

Seasonal decomposition
LSTM & Prophet models
Confidence intervals
Multi-step ahead forecasting
Performance Metrics
MAPE: 8.2%RMSE: $2,450R2: 0.91

Anomaly Detection

Identify outliers and anomalies in sensor data, transactions, or system logs

Isolation Forest algorithm
Real-time detection
Adaptive thresholds
Explainable anomalies
Performance Metrics
PRECISION: 94.2%RECALL: 89.1%F1: 0.91

How AutoML Works

1

Upload Data

Upload your dataset and select the target variable

2

Auto Feature Engineering

Automatic feature creation, selection, and transformation

3

Model Training

Train multiple models with hyperparameter optimization

4

Deploy & Monitor

Deploy best model and monitor performance