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, you will create machine learning models capable of classifying candidate exoplanets from the raw dataset.

In this homework assignment, you will need to:

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

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

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

Compare the performance of two or more classifiers to determine the best model performance.

• Exoplanet Data Source

• Scikit-Learn Tutorial Part 1

• Scikit-Learn Tutorial Part 2

• Grid Search

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

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

• Create a Jupyter Notebook and host the notebook on GitHub.

• Include a README.md file that summarizes your assumptions and findings.

• Submit the link to your GitHub project to Bootcamp Spot.