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End-to-End Machine Learning

Train a Binary Classifier

This project is part of the liveProject series End-to-End Machine Learning for Rain Prediction.
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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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.

book and video resources

When you start your liveProject, you get full access to the following books and videos for 90 days.

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

you will learn

In this liveProject, you’ll learn skills for cleaning and exploring data in preparation for training a machine learning model.

  • Manipulating data and exploratory analysis
  • Imputing missing values, engineering outliers, and one-hot encoding
  • Evaluating predictions, accuracy, and other metrics
  • Visualizing patterns with correlation pair plots and heatmaps

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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