Train a Binary Classifier

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prerequisites
basic Python • basic pandas • basic NumPy • basic Matplotlib • basic seaborn •basic scikit-learn • basic Jupyter Notebook • basics of machine learning • basics of exploratory analysis
skills learned
manipulate data and conduct exploratory analysis • input missing values • engineer outliers • evaluate predictions
Harshit Tyagi
1 week · 3-5 hours per week · INTERMEDIATE
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catalog / Data Science
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Look inside
In this liveProject, you’ll explore a pre-made dataset of meteorological records. You’ll learn the shape, size and type of data at hand, and discover factors that affect rainfall. You use scikit-learn and logistics regression to make initial predictions about future rainfall, evaluate their accuracy, and visualize emerging patterns using Seaborn and Matplotlib.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Harshit Tyagi
Harshit Tyagi has helped over a thousand students master the fundamentals of programming and data science. In his roles at OpenClassrooms and Coding Ninjas, he leverages his technical expertise to conduct workshops and help students bring their course projects to the finish line. He also has a YouTube channel, where he covers fundamental concepts in data science and Python, interview tips, and more. In addition to focusing on data science education, Harshit has developed data processing algorithms with research scientists at Yale, MIT and UCLA.

prerequisites

This liveProject is for Python data scientists who want to expand their capabilities in preparing data for machine learning. To begin this liveProject you will need to be familiar with:

TOOLS
  • Basic Python
  • Basic pandas
  • Basic NumPy
  • Basic Matplotlib
  • Basic seaborn
  • Basic scikit-learn
  • Basic Jupyter Notebook
TECHNIQUES
  • Basics of machine learning
  • Basics of exploratory analysis

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