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Naïve Bees: Predict Species from Images

Build a model that can automatically detect honey bees and bumble bees in images.

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  • 11 tasks
  • 2,641 participants
  • 1,500 XP

Project Description

Can a machine distinguish between a honey bee and a bumble bee? Being able to identify bee species from images, while challenging, would allow researchers to more quickly and effectively collect field data. In this project, you will use the Python image library Pillow to load and manipulate image data, then build a model to identify honey bees and bumble bees given an image of these insects.

This project is the second part of a series of projects that walk through working with image data, building classifiers using traditional techniques, and leveraging the power of deep learning for computer vision.

The recommended prerequisites for this project are Intermediate Python for Data Science, Introduction to Data Visualization with Python, Supervised Learning with scikit-learn, and Naïve Bees: Image Loading and Processing.

Project Tasks

  • 1Import Python libraries
  • 2Display image of each bee type
  • 3Image manipulation with rgb2grey
  • 4Histogram of oriented gradients
  • 5Create image features and flatten into a single row
  • 6Loop over images to preprocess
  • 7Scale feature matrix + PCA
  • 8Split into train and test sets
  • 9Train model
  • 10Score model
  • 11ROC curve + AUC
Peter Bull

Co-founder of DrivenData

Peter is a co-founder of DrivenData. He earned his master's in Computational Science and Engineering from Harvard’s School of Engineering and Applied Sciences. His work lies at the intersection of statistics and computer science, and he wants to help bring powerful new modeling techniques to the organizations that need them most. He previously worked as a software engineer at Microsoft and earned a BA in philosophy from Yale University.

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Emily Miller

Data Scientist at DrivenData

Emily Miller is a Data Scientist at DrivenData. Her personal passion is to combine data science with satellite imagery, text data, and other non-traditional data sources to make poverty alleviation efforts more effective. She was previously a Data Scientist at the Bill & Melinda Gates Foundation and a Data Science Fellow at Metis.

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  • Python LogoPython
  • Topics

    Data ManipulationData VisualizationMachine Learning