Train and Score with Raw Data you own this product

prerequisites
intermediate Python and scikit-learn • basics of Jupyter Notebook, pandas, and SQL
skills learned
classification with logistic regression • ML pipelines with scikit-learn • generating model pickle files with Joblib
Jayesh Patel
1 week · 8-10 hours per week · BEGINNER
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Look inside

In this liveProject, you’ll train and evaluate a machine learning model for diagnosing diabetes, and set up a pipeline for your model to run effectively. You’ll start by exploring sample data, processing features, and performing common feature engineering techniques for treating outliers or missing data. After dividing your dataset into training and testing data, you’ll train a logistic regression model using scikit-learn. You will then retrain the model with a different set of features. Finally, you’ll pick a model for scoring and build a scoring pipeline. You will test your scoring process on a scoring dataset.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Jayesh Patel
Jayesh Patel is a strategic big data leader and proven architect who successfully designed complex data processes, architected machine learning pipelines, and developed big data analytics solutions over the past 15+ years. He currently works for Rockstar Games, architecting data-driven big data platforms and artificial intelligence solutions to keep players engaged in Red Dead Redemption II and Grand Theft Auto V. He is an active senior member of the IEEE. His expertise and research in the big data space are well received in numerous international IEEE conferences. He is an editorial board member of a renowned international journal. He actively guides and reviews the research work of other scholars and professors around the world. He completed his master’s from San Diego State University in 2009.

prerequisites

This liveProject is for data scientists and engineers who are familiar with Python, the basics of machine learning, and data modeling. To begin this liveProject you will need to be familiar with the following:


TOOLS
  • Intermediate Python
  • Basics of Jupyter Notebook
  • Basics of pandas
  • Intermediate scikit-learn
  • Basics of SQL
TECHNIQUES
  • Basic file processing
  • Intermediate data processing and feature engineering
  • Intermediate machine learning pipelines
  • Basic understanding of ML development cycles
  • Basic understanding of classification with logistic regression

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