SmartPredict
  • Documentation
  • OVERVIEW
    • Presentation
      • Key features
      • Who may benefit from its use
      • The SmartPredict Graphical User Interface (GUI)
        • The SmartPredict Modules
        • Focus on the Notebooks
        • Practical shortcuts
    • Prerequisites
  • Getting started
    • Getting started (Part1) : Iris classification project
      • Project description
      • Step 1. Create the project
      • Step 2. Upload the dataset
      • Step 3. Preprocess the dataset
      • Step 4. Build the flowchart
        • Set up the flowchart
        • Configure the modules
      • Step 5. Run the build
      • Step 6. Deploy the project
      • Step 7. Make inferences with our model
      • Conclusion
  • Getting started (Part 2): Predicting the passengers' survival in the Titanic shipwreck
    • Project description
    • Step 1. Create the project
    • Step 2. Upload the dataset
    • Step 3. Preprocess the dataset
    • Step 4. Build the flowchart
    • Step 5. Run the build
    • Step 6. Deploy the pipeline
    • Step 7. Make inferences with our pipeline
  • MODULE REFERENCE
    • CORE MODULES
      • Introduction
      • Basic Operations
        • Item Saver
      • Web Services
        • Web Service IN and OUT
      • Data retrieval
        • Data fetcher
        • Data frame loader/converter
        • Image data loader
      • Data preprocessing
        • Introduction
        • Array Reshaper
        • Generic Data Preprocessor
        • Missing data handler
        • Normalizer
        • One Hot Encoder
        • Ordinal Encoder
      • Data selection
        • Features selector
        • Generic data splitter
        • Labeled data splitter
      • Training and Prediction
        • Predictor DL models
        • Predictor ML models
        • Predictor ML models (Probabilistic models)
        • Trainer ML models
        • Trainer/Evaluator DL models
      • Evaluation and fine-tuning
        • Cross Validator for ML
        • Evaluator for ML models
      • Machine Learning algorithms
        • ML modules in SmartPredict
        • Decision Tree Regressor
        • KNeighbors Classifier
        • KNeighbors Regressors
        • Linear Regressor
        • Logistic Regressor
        • MLP Regressor
        • Naive Bayes Classifier
        • Random Forest Classifier
        • Random Forest Regressor
        • Support Vector Classifier
        • Support Vector Regressor
        • XGBoost Classifier
        • XGBoost Regressor
      • Deep learning algorithms
        • Dense Neural Network
        • Recurrent Neural Networks
      • Computer Vision
        • Convolutional Recurrent Networks
        • Fully Convolutional Neural Networks
        • Face detector
        • Image IO
        • Image matcher
        • Yolo
      • Natural Language Processing
        • Introduction
        • Text cleaner
        • Text vectorizer
      • Times Series processing
        • TS features selector
      • TensorFlow API
        • LSTM Layer
        • Dense Layer
      • Helpers
        • Data/Object Logger
        • Object Selector (5 ports)
      • Conclusion
  • CUSTOM MODULES
    • Function
    • Class
    • Use cases
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On this page
  • How to use the SmartPredict Notebook ?
  • Kindred notebooks
  • SmartPredict library
  • Save current cell as a custom module
  • Save current cell as a code snippet

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  1. OVERVIEW
  2. Presentation
  3. The SmartPredict Graphical User Interface (GUI)

Focus on the Notebooks

The Notebooks are special functionalities within SmartPredict. Use them to extend your project, produce live code, import libraries and boost your custom modules.

PreviousThe SmartPredict ModulesNextPractical shortcuts

Last updated 4 years ago

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How to use the SmartPredict Notebook ?

  • Start the Kernel.

  • Click on the "Create new notebooks" button .

  • Add the name of the notebook you want to create.

  • Start coding.

  • Save it at any time.

The code shown in the .gif image is a snippet for loading the iris dataset. For testing, enter the code block below:

import smartpredict as sp
import tensorflow as tf
sp.api.dataset_load('iris_dataset')

Follow the wizard and insert the code as follows:

Kindred notebooks

If you are already familiar with kindred notebooks such as Jupyter and the like, you will find the Notebooks an interesting alternative.

If you are yet to discover the SmartPredict Notebooks, you will soon see how you can customize your projects with them and will appreciate their handy functionalities.

SmartPredict library

The SmartPredict Library is a Python library for Data Science adopting the SmartPredict Studio workflow. It is built on top of many open source frameworks. The concept is to offer an easy way for people to engage into Data Science and Machine Learning with little effort.

Save current cell as a custom module

You can export the content of a cell as a custom module's code. Both function and class types are supported.

Save current cell as a code snippet

You can save the content of a cell as a code snippet and reuse the code inside of it at any time for other projects.

The Notebooks are handy tools for creating your own snippets.
Here is the snippet that loads the iris dataset.