Creating Features

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prerequisites
intermediate Python • basics of Jupyter Notebook, pandas, and Matplotlib
skills learned
use feature engineering techniques to generate ML features from raw data • data profiling, exploratory data analysis, analyze independent/dependent variables, and visualize data patterns • generate additional features
Jayesh Patel
1 week · 6-8 hours per week · BEGINNER
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In this liveProject, you’ll process raw data to make it ready for a machine learning model to diagnose diabetes rates. You’ll use feature engineering techniques to generate ML features from raw data. To make sense of your data, you will undertake data profiling, exploratory data analysis, analyze independent/dependent variables, and visualize data patterns. You’ll evaluate the correlation between dependent and independent variables to identify relevant features. You’ll even generate additional features as needed. Additionally, you will apply feature engineering techniques such as treating missing values and outliers to make your features ready for model training.

This project is a part of the series ML Feature Engineering and Modeling using Python.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

prerequisites

This liveProject is for data scientists 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
  • Basics of Matplotlib
TECHNIQUES
  • Basic file processing
  • Intermediate data processing and feature engineering

features

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