FAufenger / machine-learning-challenge

A machine learning model capable of classifying candidate exoplanets from raw datasets is perfect to help the NASA Kepler space telescope process its planet hunting data.

machine-learning-challenge

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. To help process this data, the following models utilize machine learning capable of classifying candidate exoplanets from the raw dataset.

Preprocess the Data:

• Preprocess the dataset prior to fitting the model.
• Perform feature selection and remove unnecessary features.
• Use various Scalers to scale the numerical data.
• Separate the data into training and testing data.

Tune Model Parameters

• Use GridSearch to tune model parameters.
• Tune and compare different classifiers.

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The best result so far is the Neural Network although the other are also quite close to the score. one even 0.001 away!

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MinMax_scalar_SCV_gridSearch

• Training Data Score: 0.8472248712569139

• Testing Data Score: 0.852974828375286

• Grid Params: {'C': 50, 'gamma': 0.0001}

• Best grid Score: 0.8840364141785344

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standard_scalar_SCV_gridSearch

• Training Data Score: 0.891474346748045

• Testing Data Score: 0.8993135011441648

• Grid Params: {'C': 10, 'gamma': 0.0001}

• Best grid Score: 0.8884211790218238

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standard_scalar_logisticRegression_gridSearch

• Training Data Score: 0.8868968148006866

• Testing Data Score: 0.8947368421052632

• Grid Params: {'C': 50, 'max_iter': 200}

• Best grid Score: 0.8836536432371069

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Random Tree

• Training Data Score: 1.0

• Testing Data Score: 0.8975972540045767

• Grid Params: {'max_depth': 10, 'min_samples_split': 2}

• Best grid Score: 0.8834624396917456

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HHR

• k=19 Test Acc: 0.847
• k=19 Test Acc: 0.841

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Neural Network

• 1748/1748 - 0s - loss: 0.2598 - accuracy: 0.8976

• Loss: 0.25983195760430133, Accuracy: 0.8975972533226013

• Model: "sequential_2"

  Layer (type) Output Shape Param #

  dense_6 (Dense) (None, 100) 4100

  dense_7 (Dense) (None, 100) 10100

  dense_8 (Dense) (None, 3) 303
  
  Total params: 14,503 Trainable params: 14,503 Non-trainable params: 0

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While these models are close to 90 percent accuracy, I believe that they should be a higher before sending over for NASA to use.

I plan to contunue tuning, trying models with min max scale inplace of standard scalar and trying other models