Transform Your App with Production-Ready Machine Learning
Build sophisticated machine learning models directly in Bubble without writing a single line of code.

▶️ View tutorial: https://www.youtube.com/watch?v=JOerWisDVTM

👉 View live demo: https://nebulum-plugins.bubbleapps.io/version-test/machine_learning



What Makes This Different

Most no-code ML tools force you into rigid templates with 3-5 inputs. We give you 100 feature inputs (25 categorical + 75 numerical), custom neural network architectures, hyperparameter control, and comprehensive model evaluation, the same capabilities data scientists use in production environments.

Six Powerful ML Engines

1️⃣ Classification - Predict Categories & Make Decisions

Perfect for:

Customer churn prediction (will they cancel or stay?)
Loan approval automation (approve/deny based on financial history)

Medical diagnosis support (disease present/absent)

Fraud detection (legitimate/suspicious transactions)

Lead scoring (hot/warm/cold prospects)

Email filtering (spam/not spam)

Product recommendations (will customer buy category X?)

Employee attrition forecasting (likely to leave/stay?)

Real-world example: Train on 50+ customer attributes (demographics, purchase history, engagement metrics, support tickets) to predict which customers will churn next month with 85%+ accuracy.

2️⃣ Regression - Predict Precise Numbers

Perfect for:

Real estate price prediction
Sales forecasting (exact revenue amounts)
Demand planning (inventory quantities needed)
Customer lifetime value calculation
Insurance premium pricing
Delivery time estimation
Resource allocation (staffing levels needed)
Budget forecasting

Real-world example: Predict house prices within 5% accuracy using 75+ variables including location data, property features, market conditions, and neighborhood statistics.

3️⃣ Forecasting - Predict Future Trends

Perfect for:

Stock price prediction
Website traffic forecasting
Energy consumption planning
Seasonal sales patterns
Cash flow projections
Capacity planning
Maintenance scheduling
Supply chain optimization

Real-world example: Forecast next quarter's sales by analyzing 3+ years of historical data, accounting for seasonality, trends, and external factors.

4️⃣ Clustering - Discover Hidden Patterns

Perfect for:

Customer segmentation (group similar customers)
Market research (identify buyer personas)
Anomaly detection preparation
Product categorization
User behavior analysis
Territory optimization
Content grouping
Feature engineering

Real-world example: Automatically segment 10,000 customers into 5 distinct groups based on 40+ behavioral and demographic features, no predefined categories needed. This is an unsupervised learning model.

5️⃣ Anomaly Detection - Find Outliers & Irregularities

Perfect for:

Fraud detection (unusual transactions)
Quality control (defective products)
Network security (suspicious activity)
Equipment failure prediction
Healthcare monitoring (abnormal vitals)
Inventory discrepancies
System performance monitoring
User behavior alerts

Real-world example: Monitor 100+ transaction features in real-time to flag suspicious activity within milliseconds, before financial damage occurs.

6️⃣ Image Classification - Visual Recognition

Perfect for:

Product quality inspection
Document classification
Medical image analysis
Facial recognition
Object detection
Brand logo identification
Damage assessment
Content moderation

Real-world example: Automatically categorize thousands of product images uploaded by users into proper departments with 90%+ accuracy.

Why Data Scientists Choose This Plugin

🎯 100-Feature Input Capacity

25 categorical features (text categories like "Region", "Product Type", "Customer Tier")
75 numerical features (numbers like age, income, purchase count, days since last visit)
Handle complex real-world datasets that other tools can't touch

🧠 Customizable Neural Network Architecture

Define your own hidden layer structure (e.g., [128, 64, 32] for deep learning)
Choose activation functions (ReLU, tanh, sigmoid, linear)
Control learning rates, batch sizes, epochs
Full hyperparameter control, not dumbed down

📊 Production-Grade Model Evaluation
During training, track:

Loss & accuracy per epoch
Validation metrics
Average prediction confidence
Real-time training progress

After training, get comprehensive evaluation:

Confusion matrices (for classification)
Precision, Recall, F1-Score per class
MAE, RMSE, R² (for regression)
Per-class performance breakdowns
Confidence distributions

💾 Persistent Model Storage

Train once, predict thousands of times
Save trained models to your Bubble database
Load models instantly for predictions
Version control your ML models
No re-training required for each prediction

⚡ Real-Time Predictions

Millisecond prediction speeds
Batch prediction support
Client-side processing (no API limits)
Scalable to millions of predictions

🔧 Advanced Preprocessing

Automatic one-hot encoding for categorical data
Min-max normalization for numerical features
Handles missing data gracefully
Supports both binary and multi-class classification

Common Use Cases by Industry

E-Commerce:

Predict purchase likelihood, customer lifetime value, churn risk

Recommend products, optimize pricing, forecast inventory needs

Finance:

Credit scoring, fraud detection, loan default prediction
Portfolio optimization, trading signals, risk assessment

Healthcare:

Patient readmission prediction, treatment outcome forecasting

Disease diagnosis support, resource allocation

SaaS:

Churn prediction, usage forecasting, tier recommendations

Feature adoption analysis, support ticket classification

Real Estate:

Property valuation, market trend analysis
Investment opportunity scoring, rental yield prediction

Marketing:

Lead scoring, campaign performance prediction

Customer segmentation, lifetime value calculation

Technical Specs (For the Curious)

Engine: TensorFlow.js

Model Types: Sequential neural networks with dense layers

Optimizers: Adam (with configurable learning rate)

Loss Functions: Binary/categorical cross-entropy, MSE, MAE

Validation: Built-in train/validation split

Memory Management: Automatic tensor cleanup

Browser Support: All modern browsers (Chrome, Firefox, Safari, Edge)

Getting Started is Simple

Train: Connect your Bubble database fields to the training action

Evaluate: Review comprehensive performance metrics

Save: Store the trained model in your database

Predict: Load the model and make predictions on new data

No Python. No Jupyter notebooks. No cloud API keys. Just powerful machine learning that runs directly in your Bubble app.

Perfect For

✅ SaaS founders who need predictive features
✅ Agencies building custom ML solutions for clients
✅ Enterprises automating decision-making
✅ Startups testing ML product ideas
✅ Anyone who needs production-grade ML without a data science team

▶️ View tutorial: https://www.youtube.com/watch?v=JOerWisDVTM

👉 View live demo: https://nebulum-plugins.bubbleapps.io/version-test/machine_learning