Exoplanet-classification

Project Overview:

This project focuses on exploring and classifying exoplanets using machine learning (ML) techniques.

• Project Pipeline:
  • Exploratory Data Analysis (EDA):
    • The project visualizes the data and detects for anomalies based on which it makes certain assessments.
    • The data is further cleaned and processed so as to make it suitable for model training.
  • Modeling:
    • K-Nearest Neighbors (KNN) algorithm is used here for classification.
    • Performance Metrics:
      • Evaluated using ROC (Receiver Operating Characteristic) curves.
      • AUC (Area Under the Curve) is computed to assess the model's classification accuracy.
      • Confusion Matrix is used to analyze the performance of the classification model.
  • Handling Data Imbalance:
    • SMOTE (Synthetic Minority Over-sampling Technique) is applied to handle class imbalance and improve model performance.

Ending notes:

• The following project is my first machine-learning project so for certain concepts I have also linked ytvideos(inline-comments) that I have referred to, for better understanding of the project

Peace