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Pulsar Classification for Class Prediction

Project Objective

The goal of this project is to build a robust classification model capable of predicting whether a space observation instance is a Pulsar (1) or not a Pulsar (0).

Pulsars are rare astronomical objects, and identifying them requires high-precision modeling to handle the significant class imbalance in the dataset.

Challenge:

High imbalance due to rare nature of Pulsar stars

Dataset Overview

The dataset contains 17,898 entries with features derived from the Integrated Profile and the DM-SNR Curve of the observations.

Features:

  • Mean_Integrated: Mean of the integrated profile.
  • SD: Standard deviation of the integrated profile.
  • EK: Excess kurtosis of the integrated profile.
  • Skewness: Asymmetry of the probability distribution.
  • Mean_DMSNR_Curve: Mean of the DM-SNR curve.
  • SD_DMSNR_Curve: Standard deviation of the DM-SNR curve.
  • EK_DMSNR_Curve: Excess kurtosis of the DM-SNR curve.
  • Skewness_DMSNR_Curve: Skewness of the DM-SNR curve.
  • Class: Target variable (0: Not Pulsar, 1: Pulsar).

Note on DM-SNR Curve: Radio waves emitted from pulsars reach Earth through space filled with free electrons. Higher frequencies slow down less than lower frequencies (dispersion), creating a curve used for identification.

Data Analysis & Preprocessing

1. Data Understanding

  • Imbalance Handling: Class 0 has 16,259 entries while Class 1 only has 1,640.
  • Correlation Insights: Features like EK and Skewness show high positive correlation with the Target Class, while Mean_Integrated shows high negative correlation.

2. Data Cleaning

  • Missing Values: Handled using Median Imputation for features like SD, Skewness, and SD_DMSNR_Curve.
  • Duplicates: Identified and removed to ensure data integrity.

3. Feature Engineering & Scaling

  • Feature Selection: Dropped highly redundant features (Mean_Integrated, Skewness, Mean_DMSNR_Curve, EK_DMSNR_Curve) to reduce multicollinearity.
  • Standardization: Applied StandardScaler to normalize feature distributions.

Model Building

I utilized a Random Forest Classifier for this task.

Why Random Forest?

  • Ensemble Power: It operates by constructing multiple decision trees and outputting the average prediction, which helps mitigate overfitting.
  • Imbalance Resilience: It is highly effective at handling imbalanced datasets where one class (non-pulsars) significantly outweighs the other.

Model Parameters:

  • n_estimators: 500
  • random_state: 42
  • test_size: 20%

Evaluation Results

The model achieved high performance across all key metrics, proving that it can identify rare Pulsars without sacrificing overall accuracy.

  • MetricScoreAccuracy --> 98.35%
  • Precision (Class 1) --> 92%
  • Recall (Class 1) --> 90%
  • F1-Score (Class 1) --> 91%

Confusion Matrix:

  • True Negatives (Non-Pulsar): 3223
  • True Positives (Pulsar): 298
  • False Positives: 25
  • False Negatives: 34

Tech Stack

  • Language: Python
  • Libraries: Pandas, NumPy, Scikit-learn, Seaborn, Matplotlib