Exoplanet Exploration

Background

Over a period of nine years in deep space, the NASA Kepler space telescope has been out on a planet-hunting mission to discover hidden planets outside of our solar system.

Task

Create machine learning models capable of classifying candidate exoplanets from the raw dataset.

Process

Preprocess the Data

• Preprocessed the dataset prior to fitting the model.
• Performed feature selection and remove unnecessary features.
• Used MinMaxScaler to scale the numerical data.
• Separated the data into training and testing data.

Tune Model Parameters

• Used GridSearch to tune model parameters.
• Tune and compare different classifiers like Logistic Regression, K-Nearest Neighbours, Support Vector Machine, Random Forest Classifier.

Conclusion

After comparing all the models, it looks like Random Forest Classifier gives the best Accuracy Score.

Logistic Regression Score

• Training Data Score: 0.8411214953271028
• Testing Data Score: 0.8409610983981693

KNN Score without Gridsearch

• Training Data Score: 0.8725920274651917
• Testing Data Score: 0.8249427917620137

KNN Score with Gridsearch

• Training Data Score: 0.8725920274651917
• Testing Data Score: 0.8249427917620137

SVM without Gridsearch

• Training Data Score: 0.8439824527942018
• Testing Data Score: 0.8415331807780321

SVM with Gridsearch

• Training Data Score: 0.8901392332633988
• Testing Data Score: 0.8861556064073226

SVM Classification Report

Random Forest Classifier Score without Gridsearch

• Training Data Score: 0.996185390043868
• Testing Data Score: 0.8729977116704806

Random Forest Classifier Score with Gridsearch

• Training Data Score: 1.0
• Testing Data Score: 0.8907322654462243

Random Forest Classification report

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Resources

• Exoplanet Data Source

• Scikit-Learn Tutorial Part 1

• Scikit-Learn Tutorial Part 2

• Grid Search