Using ML to find exoplanets - Dataset from Kaggle

Source - Exoplanet Hunting in Deep Space

• 2 is an exoplanet star and 1 is a non-exoplanet-star according to the dataset description
• Change that to 1 for exoplanets and 0 for non exoplanets

• To do this we create plots for exoplanets and non exoplanets luminosity
• After doing so we can see that exoplanets have a greater luminosity than non exoplanets
• We are also comparing the mean of the luminosity so we can have a better understanding

• If we feed our model unprocessed data it will get confused and it will probably mess everything up
• First I'm scaling the data using Sklearn's StandardScaler class
• Now, I want to have our data shaped as 3D Tensors, because we're going to use a CNN model to predict the labels

• I've created a CNN with:
  • Matrix reduction part
    • One Convolution Layer (Conv1D) - with 64 filters and a kernel of size 10
    • One Pooling Layer (MaxPooling1D) - with 4 strides
  • FC part
    • Two Dense Layers (Dense) - with 64 units and the relu activation function
    • One Output Layer (Dense) - with only 1 neuron and the softmax function
• I've complied everything using the crossentropy loss function and the adam optimizer
• For the training part I choosed 4 epochs with batches of size 32
• The model automatically saves it's weights once an epoch is completed

• Getting accuracy and loss values with the basic model.evaluate(x,y)
• Getting the confusion matrix with Sklearn