{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exoplanet Hunting in Deep Space\n", "### By Ali Shannon\n", "In this module, I look at time series light intensity data provided by the Kepler Space Telescope to detect the presence of exoplanets.\n", "\n", "The fields are explained here:\n", "\n", "1. `LABEL` uses either 1 or 2, where 1 means the star has no orbiting planet and 2 means it has at least one.\n", "\n", "2. `FLUX 1 : 3192` are the light intensity data retreived from each planet over some time interval.\n", "\n", "I intend to analyze the plots then train a predictor that will understand the relationships between light intensity values and determine whether or not the star has at least one orbiting planet. \n", "\n", "For our purposes, we will assume that there are only two types of stars, ones with orbiting planets and ones with none.\n", "\n", "### Acknowledgments\n", "\n", "Data is retreived from **[Kaggle](https://www.kaggle.com/keplersmachines/kepler-labelled-time-series-data/home)**, check out their publicly available datasets for machine learning and data analysis." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd # data analysis\n", "import scipy # stats and dependancies\n", "import numpy as np # linear algebra\n", "import matplotlib.pyplot as plt # plotting library\n", "import seaborn as sns # statistics library\n", "import IPython # shell commands\n", "\n", "np.random.seed(42)\n", "\n", "%matplotlib inline\n", "\n", "df = pd.read_csv('exoTrain.csv')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
| \n", " | LABEL | \n", "FLUX.1 | \n", "FLUX.2 | \n", "FLUX.3 | \n", "FLUX.4 | \n", "FLUX.5 | \n", "FLUX.6 | \n", "FLUX.7 | \n", "FLUX.8 | \n", "FLUX.9 | \n", "... | \n", "FLUX.3188 | \n", "FLUX.3189 | \n", "FLUX.3190 | \n", "FLUX.3191 | \n", "FLUX.3192 | \n", "FLUX.3193 | \n", "FLUX.3194 | \n", "FLUX.3195 | \n", "FLUX.3196 | \n", "FLUX.3197 | \n", "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", "2 | \n", "93.85 | \n", "83.81 | \n", "20.10 | \n", "-26.98 | \n", "-39.56 | \n", "-124.71 | \n", "-135.18 | \n", "-96.27 | \n", "-79.89 | \n", "... | \n", "-78.07 | \n", "-102.15 | \n", "-102.15 | \n", "25.13 | \n", "48.57 | \n", "92.54 | \n", "39.32 | \n", "61.42 | \n", "5.08 | \n", "-39.54 | \n", "
| 1 | \n", "2 | \n", "-38.88 | \n", "-33.83 | \n", "-58.54 | \n", "-40.09 | \n", "-79.31 | \n", "-72.81 | \n", "-86.55 | \n", "-85.33 | \n", "-83.97 | \n", "... | \n", "-3.28 | \n", "-32.21 | \n", "-32.21 | \n", "-24.89 | \n", "-4.86 | \n", "0.76 | \n", "-11.70 | \n", "6.46 | \n", "16.00 | \n", "19.93 | \n", "
| 2 | \n", "2 | \n", "532.64 | \n", "535.92 | \n", "513.73 | \n", "496.92 | \n", "456.45 | \n", "466.00 | \n", "464.50 | \n", "486.39 | \n", "436.56 | \n", "... | \n", "-71.69 | \n", "13.31 | \n", "13.31 | \n", "-29.89 | \n", "-20.88 | \n", "5.06 | \n", "-11.80 | \n", "-28.91 | \n", "-70.02 | \n", "-96.67 | \n", "
| 3 | \n", "2 | \n", "326.52 | \n", "347.39 | \n", "302.35 | \n", "298.13 | \n", "317.74 | \n", "312.70 | \n", "322.33 | \n", "311.31 | \n", "312.42 | \n", "... | \n", "5.71 | \n", "-3.73 | \n", "-3.73 | \n", "30.05 | \n", "20.03 | \n", "-12.67 | \n", "-8.77 | \n", "-17.31 | \n", "-17.35 | \n", "13.98 | \n", "
| 4 | \n", "2 | \n", "-1107.21 | \n", "-1112.59 | \n", "-1118.95 | \n", "-1095.10 | \n", "-1057.55 | \n", "-1034.48 | \n", "-998.34 | \n", "-1022.71 | \n", "-989.57 | \n", "... | \n", "-594.37 | \n", "-401.66 | \n", "-401.66 | \n", "-357.24 | \n", "-443.76 | \n", "-438.54 | \n", "-399.71 | \n", "-384.65 | \n", "-411.79 | \n", "-510.54 | \n", "
5 rows × 3198 columns
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