Classify The Radio Signals From Outer Space | Sample Paper AIML



Problem Statement

The dataset consists of 2-Dimensional spectrograms of radio signals from space collected at the SETI Institute by the Allen Telescope Array. The objective is to classify the radio signals from outer space into one of four classes.



Dataset Description

SETI Dataset


Training Data:

  • train_images: Normalized values of Pixels

  • train_labels: Stored as One-Hot Encoded data


Validation Data:

  • val_images: Normalized values of Pixels

  • val_labels: Stored as One-Hot Encoded data


Classes: “squiggle”, “narrowband”, “narrowbanddrd”, and “noise”


.ipynb file

As a part of this test, you will be performing the following tasks:

  • Prepare a detailed python notebook using CNN for classifying the radio signals from deep space using Keras from the SETI Dataset

  • Import Required Libraries

  • Load and Pre-process the dataset

  • Visualize the dataset

  • Create Training and Validation Data Generators

  • Design a Convolutional Neural Network (CNN) Model

  • Compile the Model

  • Train the Model

  • Evaluate the Mode


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