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, you will create machine learning models capable of classifying candidate exoplanets from the raw dataset. In this homework assignment, you will need to:

Preprocess the raw data Tune the models Compare two or more models

Instructions

Preprocess the Data

Preprocess the 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.

Tune Model Parameters

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

Reporting

Create a README that reports a comparison of each model's performance as well as a summary about your findings and any assumptions you can make based on your model (is your model good enough to predict new exoplanets? Why or why not? What would make your model be better at predicting new exoplanets?).

Resources

Exoplanet Data Source

Scikit-Learn Tutorial Part 1

Scikit-Learn Tutorial Part 2

Grid Search

Hints and Considerations

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.

Submission

Create a Jupyter Notebook for each model and host the notebooks on GitHub.

Create a file for your best model and push to GitHub

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

Submit the link to your GitHub project to Bootcamp Spot.