Build your machine learning pipeline in these 4 simple steps (Loan Prediction scikit-learn example)

Are you really interested in learning how you can develop you own machine learning pipeline from start to finish.

The ML pipeline consist of following components (but not limited to)

• Data Ingest
• Data cleaning and machine learning readiness
• Feature Engineering
• Data Augmentation
• Machine Learning Process and building the model
• Machine Learning model improvement
• Saving Machine Learning Model for reuse outside the pipeline
• Reusing (reading from storage) the model for prediction

In this end to end machine learning pipe line we will be using the following example: Example: Loan Prediction with scikit-learn

In this tutorial, you will learn the following:

• Tutorial Intro :

• Jupyter Notebook #1.

  • Data Ingest
  • Data cleaning and machine learning readiness

• Machine Learning Process and building the model

• Machine Learning model improvement

• Saving scikit model to disk and reload them

• Jupyter Notebook #2.

  • Feature Engineering

• Jupyter Notebook #3.

• Data Augmentation

• Jupyter Notebook #4. -

  • Saving Machine Learning Model for reuse outside the pipeline
  • Reusing (reading from storage) the model for prediction

Content Timeline:

• (00:00) Start of the Video
• (00:08) Intro
• (04:06) Jupyter Notebook #1 - Data Ingest, cleaning, ML algos
• (31:56) Jupyter Notebook #2 - Adding Feature Engineering
• ( 45:14) Jupyter Notebook #3 - Data Augmentation
• (53:01) Jupyter Notebook #4 - Using saved model and scoring with test data
• (57:50) Recap

GitHub URL: https://github.com/prodramp/publiccode/tree/master/loan-prediction

Please visit: https://prodramp.com @prodramp https://www.linkedin.com/company/prodramp

Content Creator: Avkash Chauhan (@avkashchauhan) https://www.linkedin.com/in/avkashchauhan

Tags: #machinelearning #scikit-learn #ai #artificialintelligence #jupyternotebook #workbook #notebook #webdevelopment, #frontend #react, #chakraui, #layout, #fullstackdevelopment #pandas #dataanalysis #python #bigdata #machinelearning #artificialintelligence #ai #ml #dataanalysis ... https://www.youtube.com/watch?v=OecQ2f5wifA