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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  • Prior knowledge
  • Modeling basics

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  1. OVERVIEW

Prerequisites

This page discusses about the preparation and prerequisites needed beforehand to have the best start in modeling with SmartPredict.

PreviousPractical shortcutsNextGetting started (Part1) : Iris classification project

Last updated 5 years ago

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Prior knowledge

Before starting to model, we need to acquire a few basics .

Some notions in data analytics, reporting and interpretation are needed. That said, since SmartPredict is so easy to use, advanced proficiency in those fields is in no way compulsory. Thanks to its intuitive handling, even beginners in machine learning can utilize it very well.

Prior knowledge of Python language is necessary if you want to extend your project capacities with some specific functions by creating custom modules for instance or using the SmartPredict' s Notebook for fully or partially coded projects.

As it is an online platform, there is no special hardware nor software requirements . However, for a smooth experience , it is always safer to ensure that you have a good internet connection.

Modeling basics

Modeling a machine learning project can be overwhelming without the right method, tools and process arrangement.

As you surely know, there are 4 great stages involved in the task of ML modeling, simply stated:

  1. Data processing (including pipeline processing)

  2. Model building, training, and fine tuning

  3. Model deployment

  4. and Model testing.

The user needs thus to acquire a few Machine Learning basics in advance . This is especially relevant for the choice of the appropriate algorithms and methods applied to a particular kind of project.

Anyway, tooltips are there to accompany the user with straightforward instructions, so there is no major need to worry about.

Happy modeling for all with !

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SmartPredict