Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Planet: Understanding the Amazon from Space dataset is a multi-label classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: Planet, designer and builder of the world’s largest constellation of Earth-imaging satellites collaborated with its Brazilian partner SCCON in challenging Kaggle participants to label satellite image chips with atmospheric conditions and various classes of land cover/land use. The resulting models will help the global community better understand deforestation conditions and how to respond to them.

The purpose of this modeling exercise is to construct an end-to-end template for solving multi-label machine learning problems. The series of scripting exercises will replicate Dr. Jason Brownlee’s blog post on this topic to build a robust template for future similar problems.

In this Take1 iteration, we will construct the necessary script segments to download and load the image files available on Kaggle’s website.

ANALYSIS: In this Take1 iteration, we could successfully download and pre-process the image files from Kaggle. We will input the processed datasets into the TensorFlow and observe the results in Take2.

CONCLUSION: More script segments will be forthcoming to process the images and make the predictions.

Dataset Used: Planet: Understanding the Amazon from Space

Dataset ML Model: Multi-label classification with numerical attributes

Dataset Reference: https://www.kaggle.com/c/planet-understanding-the-amazon-from-space/data

One potential source of performance benchmarks: https://www.kaggle.com/c/planet-understanding-the-amazon-from-space/leaderboard

The HTML formatted report can be found here on GitHub.