Skip to content

Latest commit

 

History

History
70 lines (46 loc) · 3.31 KB

README.md

File metadata and controls

70 lines (46 loc) · 3.31 KB
Raw

Binary Classification (Mines vs. Rocks)

Hex.pm PyPI - Python Version

Twitter URL

In this project you will discover how to effectively use the Keras library in your machine learning project by working through a binary classification project step-by-step.

Getting Started

Description of the Dataset

The dataset we will use in this tutorial is the Sonar dataset.

This is a dataset that describes sonar chirp returns bouncing off different services. The 60 input variables are the strength of the returns at different angles. It is a binary classification problem that requires a model to differentiate rocks from metal cylinders.

You can learn more about this dataset on the UCI Machine Learning repository: https://archive.ics.uci.edu/ml/datasets/Connectionist+Bench+(Sonar,+Mines+vs.+Rocks)

It is a well-understood dataset. All of the variables are continuous and generally in the range of 0 to 1. The output variable is a string “M” for mine and “R” for rock, which will need to be converted to integers 1 and 0.

What we achieved doing this Project for the Navy:

In this post, you discovered the three API styles used in Keras Deep Learning library in Python.

You learned how you can work through a binary classification problem step-by-step with Keras, specifically:

  • How to load and prepare data for use in Keras.
  • How to create a baseline neural network model.
  • How to evaluate a Keras model using scikit-learn and stratified k-fold cross validation.
  • How data preparation schemes can lift the performance of your models.
  • How experiments adjusting the network topology can lift model performance.

Prerequisites

What things you need to install the software and how to install them

puthon IDE
jupyter notebook

Built With

  • python - Programming Language
  • tensorflow - TensorFlow is an open-source machine learning library for research and production
  • keras - Keras is a high-level neural networks API
  • sklearn - Scikit-learn is a free software machine learning library for the Python
  • numpy - NumPy is the fundamental package for scientific computing
  • pandas - Pandas is a software library used for data manipulation and analysis

Authors

  • M.Junaid Fiaz - JD

License

This project is licensed under the APACHE License - see the LICENSE.md file for details