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 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.

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

• Training Data Score: 0.8472248712569139

• Testing Data Score: 0.852974828375286

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

• Best grid Score: 0.8840364141785344

• Training Data Score: 0.891474346748045

• Testing Data Score: 0.8993135011441648

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

• Best grid Score: 0.8884211790218238

• Training Data Score: 0.8868968148006866

• Testing Data Score: 0.8947368421052632

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

• Best grid Score: 0.8836536432371069

• Training Data Score: 1.0

• Testing Data Score: 0.8975972540045767

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

• Best grid Score: 0.8834624396917456

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

• 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