Predictly (https://predictly.cloud)
- Upload your tabular data
- Choose what you want to predict
- Get solid predictions and analytics in minutes
Predictly implements the most common tabular prediction tasks:
- Regression — predicting a number
- Binary Classification — predicting 2 categories like True/False
- Multiclass Classification — predicting from many categories like A/B/C
Predictly works with tabular data, the kind found in CSV files and spreadsheets:
- Each row represents one sample (e.g. a house or a customer)
- Each column represents a feature
- One column is the target — the value that you want Predictly to predict
Predictly is designed for:
- Product and operations teams who want to test out ideas
- Engineers who want to prototype and get fast, solid results
- Anyone who wants predictions and analytics without coding
It is especially useful when:
- You want results quickly
- You value consistency and reliability
- You want transparency instead of black boxes
Predictly does not try to be everything.
- It is not a deep-learning research platform
- It is not optimized for large datasets
- It is not a replacement for custom ML engineering
Predictly focuses on the most common, practical tabular problems — and does them well.
Predictly is intentionally simple. Rather than exposing multiple tuning knobs, it focuses on:
- Sensible defaults
- Clear steps
- Strong validation
This makes it easy to get useful results without needing to be a machine-learning expert.
- Flags and imputes missing values
- Reduces the impact of outliers
- Generates polynomial features from high-impact features
- Encodes and scales features automatically
- Balances model complexity to avoid underfitting and overfitting
Predictly evaluates models using out-of-fold (OOF) validation.
Your data is split into multiple parts. Models are trained on some parts and tested on others.
This provides a more realistic picture of how a model will perform on unseen data, not just the data it has already seen.
After modeling and training, Predictly shows several key outputs:
- Training Metric — How well the model fits the data it trained on
- Validation Metric — How well the model performs on unseen data
- Robustness — Measures how stable performance is across folds
- Baseline Comparison — How the model performs relative to a naive baseline
- Model Variation — Variation across different folds
- Where the Model Works Best — Performance across different segments of data
- Feature Effects — Which features matter and how they influence predictions
- Data Health — Highlights issues in your data
- Predictions — Final predicted values for your prediction dataset
