DaniSanta/machine-learning-challenge

Machine Learning

Machine Learning - 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.

To help process this data, I will create machine learning models capable of classifying candidate exoplanets from the raw dataset.

1. Preprocess the raw data
2. Tune the models
3. Compare two or more models

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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

• UsedGridSearch to tune model parameters.
• Tuned and compared at least two different classifiers.

Reporting

• Created a README that reports a comparison of each model's performance as well as a summary about my findings and any assumptions I made based on my model.

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Resources

• Exoplanet Data Source

• Scikit-Learn Tutorial Part 1

• Scikit-Learn Tutorial Part 2

• Grid Search

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Considerations Thoughout The Process

• Start by cleaning the data, removing unnecessary columns, and scaling the data.

• Not all variables are significant be sure to remove any insignificant variables.

• Make sure your sklearn package is up to date.

• Try a simple model first, and then tune the model using GridSearch.