diff --git "a/src/StarSystemClassification.ipynb" "b/src/StarSystemClassification.ipynb"
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+++ "b/src/StarSystemClassification.ipynb"
@@ -0,0 +1,6926 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "4473f522-21ee-4645-a548-53a3b096216a",
+ "metadata": {},
+ "source": [
+ "# Classification"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d26c9e84-69ce-4954-859b-2097448420d3",
+ "metadata": {},
+ "source": [
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d4df2e43-6542-4363-86e0-c2e06fdec4b2",
+ "metadata": {},
+ "source": [
+ "### Dataset Download \n",
+ "You can download the CSV file here: \n",
+ "[https://www.kaggle.com/competitions/orbyx-ml-challenge-star-system-classification/data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ccfafdfc-5cdd-4684-87d9-cb6fdda3dd3c",
+ "metadata": {},
+ "source": [
+ "### Intrduction\n",
+ "This project focuses on classifying star systems based on astrophysical and galactic features.\n",
+ "The dataset contains numerical and categorical information such as star size, brightness, mass, spectral class, planetary configuration, and galaxy region.\n",
+ "The task is a multiclass classification problem with four possible system types: Habitable, Young, Old, and Exotic.\n",
+ "The goal is to build a machine learning model that can accurately predict the correct system type using these features."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00b951ad-505d-4c0c-9d6e-ea0273f6caaf",
+ "metadata": {},
+ "source": [
+ "### Import Libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 91,
+ "id": "0e312045-dfbe-4baf-b001-40f6ec8b9436",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
+ "from sklearn.linear_model import LogisticRegression\n",
+ "from sklearn.ensemble import RandomForestClassifier\n",
+ "from lightgbm import LGBMClassifier\n",
+ "from xgboost import XGBClassifier\n",
+ "from sklearn.preprocessing import LabelEncoder\n",
+ "from sklearn.metrics import roc_auc_score\n",
+ "from sklearn.metrics import roc_curve\n",
+ "import joblib\n",
+ "import warnings\n",
+ "warnings.filterwarnings('ignore')\n",
+ "pd.set_option('display.max_columns',100)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4fd40b68-957d-4c6e-bcf8-668e988c6f5b",
+ "metadata": {},
+ "source": [
+ "### Load Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "0afb6131-a3e9-4bdd-8529-11ee8c18096f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df1=pd.read_csv('train.csv')\n",
+ "df2=pd.read_csv('test.csv')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "366af6f3-4560-4cd8-863d-08898f5768dd",
+ "metadata": {},
+ "source": [
+ "### EDA"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "97edfcc0-d304-4851-85db-67bc4690dc41",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " star_size \n",
+ " star_brightness \n",
+ " galaxy_region \n",
+ " distance_from_earth \n",
+ " galaxy_type \n",
+ " star_spectral_class \n",
+ " planet_configuration \n",
+ " stellar_activity_class \n",
+ " star_mass \n",
+ " metallicity \n",
+ " system_type \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 1.803381 \n",
+ " 4.217862 \n",
+ " 0.0 \n",
+ " 57.986415 \n",
+ " 0 \n",
+ " B \n",
+ " Gas_Giant_Dominated \n",
+ " Medium \n",
+ " 2.387225 \n",
+ " -0.010223 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 0.820914 \n",
+ " 1.124089 \n",
+ " 0.0 \n",
+ " 96.923329 \n",
+ " 1 \n",
+ " F \n",
+ " Rocky_Dominated \n",
+ " Medium \n",
+ " 1.058428 \n",
+ " -0.009996 \n",
+ " 0 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 0.745982 \n",
+ " 0.884981 \n",
+ " 1.0 \n",
+ " 5.000000 \n",
+ " 0 \n",
+ " G \n",
+ " Compact_System \n",
+ " Low \n",
+ " 1.108441 \n",
+ " 0.042653 \n",
+ " 0 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 0.274449 \n",
+ " 0.381716 \n",
+ " 1.0 \n",
+ " 175.711687 \n",
+ " 0 \n",
+ " G \n",
+ " Rocky_Dominated \n",
+ " Low \n",
+ " 0.363638 \n",
+ " 0.008441 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 0.943031 \n",
+ " 7.491241 \n",
+ " 2.0 \n",
+ " 64.658718 \n",
+ " 0 \n",
+ " B \n",
+ " Compact_System \n",
+ " Low \n",
+ " 1.641979 \n",
+ " -0.006315 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " star_size star_brightness galaxy_region distance_from_earth \\\n",
+ "0 1.803381 4.217862 0.0 57.986415 \n",
+ "1 0.820914 1.124089 0.0 96.923329 \n",
+ "2 0.745982 0.884981 1.0 5.000000 \n",
+ "3 0.274449 0.381716 1.0 175.711687 \n",
+ "4 0.943031 7.491241 2.0 64.658718 \n",
+ "\n",
+ " galaxy_type star_spectral_class planet_configuration \\\n",
+ "0 0 B Gas_Giant_Dominated \n",
+ "1 1 F Rocky_Dominated \n",
+ "2 0 G Compact_System \n",
+ "3 0 G Rocky_Dominated \n",
+ "4 0 B Compact_System \n",
+ "\n",
+ " stellar_activity_class star_mass metallicity system_type \n",
+ "0 Medium 2.387225 -0.010223 1 \n",
+ "1 Medium 1.058428 -0.009996 0 \n",
+ "2 Low 1.108441 0.042653 0 \n",
+ "3 Low 0.363638 0.008441 2 \n",
+ "4 Low 1.641979 -0.006315 1 "
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "8c909c03-f93f-4063-9587-f0def06ccb93",
+ "metadata": {},
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\n",
+ " \n",
+ " \n",
+ " \n",
+ " star_size \n",
+ " star_brightness \n",
+ " galaxy_region \n",
+ " distance_from_earth \n",
+ " galaxy_type \n",
+ " star_spectral_class \n",
+ " planet_configuration \n",
+ " stellar_activity_class \n",
+ " star_mass \n",
+ " metallicity \n",
+ " system_type \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 6450 \n",
+ " 1.434594 \n",
+ " 1.025439 \n",
+ " 1.0 \n",
+ " 63.942492 \n",
+ " 0 \n",
+ " A \n",
+ " Mixed_System \n",
+ " Low \n",
+ " 1.377966 \n",
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+ " 0 \n",
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+ " 6451 \n",
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+ " 0 \n",
+ " A \n",
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+ " High \n",
+ " 2.142983 \n",
+ " 0.024208 \n",
+ " 0 \n",
+ " \n",
+ " \n",
+ " 6452 \n",
+ " 2.117016 \n",
+ " 2.254183 \n",
+ " 1.0 \n",
+ " 12.874281 \n",
+ " 1 \n",
+ " A \n",
+ " Gas_Giant_Dominated \n",
+ " Medium \n",
+ " 3.882778 \n",
+ " 0.024243 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " 6453 \n",
+ " 1.447891 \n",
+ " 1.489474 \n",
+ " 1.0 \n",
+ " 60.649748 \n",
+ " 1 \n",
+ " K \n",
+ " Compact_System \n",
+ " Low \n",
+ " 1.805276 \n",
+ " -0.003033 \n",
+ " 0 \n",
+ " \n",
+ " \n",
+ " 6454 \n",
+ " 0.503435 \n",
+ " 0.945974 \n",
+ " 0.0 \n",
+ " 65.209838 \n",
+ " 0 \n",
+ " B \n",
+ " Mixed_System \n",
+ " Low \n",
+ " 0.727459 \n",
+ " 0.068651 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " star_size star_brightness galaxy_region distance_from_earth \\\n",
+ "6450 1.434594 1.025439 1.0 63.942492 \n",
+ "6451 1.505925 1.578520 1.0 34.109506 \n",
+ "6452 2.117016 2.254183 1.0 12.874281 \n",
+ "6453 1.447891 1.489474 1.0 60.649748 \n",
+ "6454 0.503435 0.945974 0.0 65.209838 \n",
+ "\n",
+ " galaxy_type star_spectral_class planet_configuration \\\n",
+ "6450 0 A Mixed_System \n",
+ "6451 0 A Mixed_System \n",
+ "6452 1 A Gas_Giant_Dominated \n",
+ "6453 1 K Compact_System \n",
+ "6454 0 B Mixed_System \n",
+ "\n",
+ " stellar_activity_class star_mass metallicity system_type \n",
+ "6450 Low 1.377966 0.044034 0 \n",
+ "6451 High 2.142983 0.024208 0 \n",
+ "6452 Medium 3.882778 0.024243 1 \n",
+ "6453 Low 1.805276 -0.003033 0 \n",
+ "6454 Low 0.727459 0.068651 2 "
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1.tail()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "3675289e-a650-4f44-be6f-a77025e6aa29",
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+ " 4.258273 \n",
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+ " 0.368782 \n",
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+ " 0 \n",
+ " O \n",
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+ ],
+ "text/plain": [
+ " id star_size star_brightness galaxy_region distance_from_earth \\\n",
+ "0 0 0.784214 0.983817 0.0 58.720489 \n",
+ "1 1 0.608677 1.253673 2.0 75.323537 \n",
+ "2 2 0.735164 2.463886 1.0 24.312129 \n",
+ "3 3 3.024780 1.279998 1.0 72.555450 \n",
+ "4 4 0.368782 7.304902 2.0 110.342929 \n",
+ "\n",
+ " galaxy_type star_spectral_class planet_configuration \\\n",
+ "0 0 G Rocky_Dominated \n",
+ "1 1 M Single_Planet \n",
+ "2 2 B Compact_System \n",
+ "3 2 A Single_Planet \n",
+ "4 0 O Mixed_System \n",
+ "\n",
+ " stellar_activity_class star_mass metallicity \n",
+ "0 Low 1.012590 0.030542 \n",
+ "1 Low 1.011377 0.024640 \n",
+ "2 Medium 0.773153 -0.008818 \n",
+ "3 Medium 4.258273 0.032196 \n",
+ "4 Low 0.346554 0.041147 "
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df2.head()"
+ ]
+ },
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+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "a6f97445-3da0-4de2-ba93-b23835649b13",
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+ " galaxy_region \n",
+ " distance_from_earth \n",
+ " galaxy_type \n",
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+ " 0.089083 \n",
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+ " id star_size star_brightness galaxy_region distance_from_earth \\\n",
+ "1317 1317 0.487870 0.020000 2.0 197.060390 \n",
+ "1318 1318 1.321548 1.153974 1.0 110.597524 \n",
+ "1319 1319 0.259288 15.000000 0.0 357.020972 \n",
+ "1320 1320 0.463398 1.667550 1.0 196.356927 \n",
+ "1321 1321 1.235463 1.334183 0.0 27.965312 \n",
+ "\n",
+ " galaxy_type star_spectral_class planet_configuration \\\n",
+ "1317 2 B Compact_System \n",
+ "1318 0 F Mixed_System \n",
+ "1319 1 G Mixed_System \n",
+ "1320 1 K Single_Planet \n",
+ "1321 0 F Mixed_System \n",
+ "\n",
+ " stellar_activity_class star_mass metallicity \n",
+ "1317 High 0.690690 0.025308 \n",
+ "1318 Low 1.227053 0.007132 \n",
+ "1319 High 4.079614 0.055164 \n",
+ "1320 High 0.089083 0.022989 \n",
+ "1321 Low 1.178063 0.015512 "
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df2.tail()"
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+ "id": "2dc8ff6c-adbe-4dfc-8f19-6ef85dbd6b2a",
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+ "metadata": {},
+ "output_type": "execute_result"
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+ "df2.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "82168297-21bd-47b4-b315-a8abd9961868",
+ "metadata": {},
+ "outputs": [
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+ "data": {
+ "text/plain": [
+ "star_size 0\n",
+ "star_brightness 194\n",
+ "galaxy_region 65\n",
+ "distance_from_earth 71\n",
+ "galaxy_type 0\n",
+ "star_spectral_class 0\n",
+ "planet_configuration 0\n",
+ "stellar_activity_class 0\n",
+ "star_mass 0\n",
+ "metallicity 0\n",
+ "system_type 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1.isnull().sum()"
+ ]
+ },
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+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "9dee297b-91cf-4d4a-82b1-d6c0506eaec7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "id 0\n",
+ "star_size 0\n",
+ "star_brightness 40\n",
+ "galaxy_region 13\n",
+ "distance_from_earth 16\n",
+ "galaxy_type 0\n",
+ "star_spectral_class 0\n",
+ "planet_configuration 0\n",
+ "stellar_activity_class 0\n",
+ "star_mass 0\n",
+ "metallicity 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df2.isnull().sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "e342b540-5ab1-4f7c-9858-e7bb0029fc8e",
+ "metadata": {},
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+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "RangeIndex: 6455 entries, 0 to 6454\n",
+ "Data columns (total 11 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 star_size 6455 non-null float64\n",
+ " 1 star_brightness 6261 non-null float64\n",
+ " 2 galaxy_region 6390 non-null float64\n",
+ " 3 distance_from_earth 6384 non-null float64\n",
+ " 4 galaxy_type 6455 non-null int64 \n",
+ " 5 star_spectral_class 6455 non-null object \n",
+ " 6 planet_configuration 6455 non-null object \n",
+ " 7 stellar_activity_class 6455 non-null object \n",
+ " 8 star_mass 6455 non-null float64\n",
+ " 9 metallicity 6455 non-null float64\n",
+ " 10 system_type 6455 non-null int64 \n",
+ "dtypes: float64(6), int64(2), object(3)\n",
+ "memory usage: 554.9+ KB\n"
+ ]
+ }
+ ],
+ "source": [
+ "df1.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "c9e9eb01-d712-46fd-827b-62f5dd01aae6",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "RangeIndex: 1322 entries, 0 to 1321\n",
+ "Data columns (total 11 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 id 1322 non-null int64 \n",
+ " 1 star_size 1322 non-null float64\n",
+ " 2 star_brightness 1282 non-null float64\n",
+ " 3 galaxy_region 1309 non-null float64\n",
+ " 4 distance_from_earth 1306 non-null float64\n",
+ " 5 galaxy_type 1322 non-null int64 \n",
+ " 6 star_spectral_class 1322 non-null object \n",
+ " 7 planet_configuration 1322 non-null object \n",
+ " 8 stellar_activity_class 1322 non-null object \n",
+ " 9 star_mass 1322 non-null float64\n",
+ " 10 metallicity 1322 non-null float64\n",
+ "dtypes: float64(6), int64(2), object(3)\n",
+ "memory usage: 113.7+ KB\n"
+ ]
+ }
+ ],
+ "source": [
+ "df2.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "37e8a4f5-ac3d-498c-a50d-1cf20b223a80",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df=pd.concat([df1,df2])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "fde33d28-af52-486d-9cde-1f381d5d9cb3",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "star_brightness\n",
+ "0.020000 362\n",
+ "15.000000 97\n",
+ "1.339762 1\n",
+ "1.511891 1\n",
+ "0.450969 1\n",
+ " ... \n",
+ "3.067567 1\n",
+ "5.027238 1\n",
+ "1.153974 1\n",
+ "1.667550 1\n",
+ "2.528870 1\n",
+ "Name: count, Length: 7086, dtype: int64"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df['star_brightness'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "3d1f6da6-45ea-4489-95a0-e555f03b5c83",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "galaxy_region\n",
+ "1.0 4041\n",
+ "0.0 2595\n",
+ "2.0 1063\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df['galaxy_region'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "3a001e3a-419b-4c44-ac2b-623187a2a16b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "distance_from_earth\n",
+ "5.000000 813\n",
+ "600.000000 6\n",
+ "29.970359 1\n",
+ "200.650266 1\n",
+ "174.942733 1\n",
+ " ... \n",
+ "197.060390 1\n",
+ "110.597524 1\n",
+ "357.020972 1\n",
+ "196.356927 1\n",
+ "86.851596 1\n",
+ "Name: count, Length: 6873, dtype: int64"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df['distance_from_earth'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "6b279336-e3c3-485b-8628-3c143b3796fb",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " star_size \n",
+ " star_brightness \n",
+ " galaxy_region \n",
+ " distance_from_earth \n",
+ " galaxy_type \n",
+ " star_mass \n",
+ " metallicity \n",
+ " system_type \n",
+ " id \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " count \n",
+ " 7777.000000 \n",
+ " 7543.000000 \n",
+ " 7699.000000 \n",
+ " 7690.000000 \n",
+ " 7777.000000 \n",
+ " 7777.000000 \n",
+ " 7777.000000 \n",
+ " 6455.000000 \n",
+ " 1322.000000 \n",
+ " \n",
+ " \n",
+ " mean \n",
+ " 1.289816 \n",
+ " 2.155972 \n",
+ " 0.801013 \n",
+ " 126.046335 \n",
+ " 0.605375 \n",
+ " 1.579010 \n",
+ " 0.019737 \n",
+ " 1.349342 \n",
+ " 660.500000 \n",
+ " \n",
+ " \n",
+ " std \n",
+ " 0.885097 \n",
+ " 2.672936 \n",
+ " 0.659990 \n",
+ " 103.982371 \n",
+ " 0.763585 \n",
+ " 1.150367 \n",
+ " 0.019644 \n",
+ " 1.117461 \n",
+ " 381.772838 \n",
+ " \n",
+ " \n",
+ " min \n",
+ " 0.100000 \n",
+ " 0.020000 \n",
+ " 0.000000 \n",
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+ " -1.429165 \n",
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+ " \n",
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+ " 25% \n",
+ " 0.671195 \n",
+ " 0.527842 \n",
+ " 0.000000 \n",
+ " 53.092301 \n",
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+ " 0.807847 \n",
+ " 0.007086 \n",
+ " 0.000000 \n",
+ " 330.250000 \n",
+ " \n",
+ " \n",
+ " 50% \n",
+ " 1.085302 \n",
+ " 1.256857 \n",
+ " 1.000000 \n",
+ " 94.904325 \n",
+ " 0.000000 \n",
+ " 1.289207 \n",
+ " 0.019959 \n",
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+ " \n",
+ " \n",
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+ " 1.693843 \n",
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+ " 1.000000 \n",
+ " 180.896818 \n",
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+ " 2.050224 \n",
+ " 0.032622 \n",
+ " 2.000000 \n",
+ " 990.750000 \n",
+ " \n",
+ " \n",
+ " max \n",
+ " 4.000000 \n",
+ " 15.000000 \n",
+ " 2.000000 \n",
+ " 600.000000 \n",
+ " 2.000000 \n",
+ " 10.088574 \n",
+ " 0.121348 \n",
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+ " 1321.000000 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " star_size star_brightness galaxy_region distance_from_earth \\\n",
+ "count 7777.000000 7543.000000 7699.000000 7690.000000 \n",
+ "mean 1.289816 2.155972 0.801013 126.046335 \n",
+ "std 0.885097 2.672936 0.659990 103.982371 \n",
+ "min 0.100000 0.020000 0.000000 5.000000 \n",
+ "25% 0.671195 0.527842 0.000000 53.092301 \n",
+ "50% 1.085302 1.256857 1.000000 94.904325 \n",
+ "75% 1.693843 2.636634 1.000000 180.896818 \n",
+ "max 4.000000 15.000000 2.000000 600.000000 \n",
+ "\n",
+ " galaxy_type star_mass metallicity system_type id \n",
+ "count 7777.000000 7777.000000 7777.000000 6455.000000 1322.000000 \n",
+ "mean 0.605375 1.579010 0.019737 1.349342 660.500000 \n",
+ "std 0.763585 1.150367 0.019644 1.117461 381.772838 \n",
+ "min 0.000000 -1.429165 -0.064158 0.000000 0.000000 \n",
+ "25% 0.000000 0.807847 0.007086 0.000000 330.250000 \n",
+ "50% 0.000000 1.289207 0.019959 1.000000 660.500000 \n",
+ "75% 1.000000 2.050224 0.032622 2.000000 990.750000 \n",
+ "max 2.000000 10.088574 0.121348 3.000000 1321.000000 "
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "1d5ee955-7c3f-4141-8f1e-9a1655ed00a1",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
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L1IWVVW7ExHzEixcvERjkieB799P4alBK3NyOwvOSj/K34f9yh41NotyRN0+SXwQkFPY4Qpm5rbpOXLtHRj6N6xtJ8ouVSk7y9r6OqtWawdzcDIaGBoiMfAqPM67w8rqW7L69va8jKioKxYsXhY/PjdQrTWnm5nZU7TEmcT/Jm9cq2RwDJJdDPq0TGZ9DEvcl609latWqCmvrPPD3P688r6+vjzlzJmHo0L4oWfIHZbmpaU64uq7H69dv0bnzAMTExKS16vSFPHkS17aJZ2TmzWuVZOZmvPDHkbCxVi2fJ68VoqOj8fTpc5XlQ4f1w6jRg9CuTW/cuslr1GZWmSmXxEt4PnLpkjeuXz+lcj6SmLf3jf8dc4rwmKMFT5VcovpLkTx5rJL8oiRe+ONIWCfJJZZqc8ngX/pixKiB6OD0U7K5pGCh/KhTryb69Pgl/RUhIiKib0a67u4VGhqK6OjoJMs/fvyIx48fp7r+hAkT8OLFC5WHXjaz9ISSKbx+/QZ3795THrdu+SM09DEaNayjlDEwMECd2tVx/vzlZLdz4eIVNGpYW2VZ40Z1cP6C6joL/5mJdk7N0bjpj7inwUCkpWVuFCqUD6GcFfFVREdHw9v7Bho0+EFlef0GP+DihStq17l00Rv1E5Vv2LA2vLyuK4NFFy96o0HDRGUa1cbFC17p3m88PT09ZdA1oSdPnuHFi5eoW7cG8ua1woEDunVDMm17/foN7gbeUx6+vvG541MeMDAwQO3a1XAhhTa8eMELDRPljkaN6ijrREdHw8vrepL80rBhbVy4kDQnvXz5CpGRT1H8uyKoVKkc9ru5J7vvUqUcYGhoqDJoQl/W69dvlIGhwMBg+PoGIDQ0HA0T5AON+slFLzRokLgPqPYTb+/rSco0aFBbKbN5825UqdIU1ao1Vx6PHoVhwYL/Q+vWn26uamZmCje3jYiKikLHjv1Urh1PX190dDSuet9Evfq1VJbXa1ALly56qV3H85I36jVQLV+/wQ/w8b6h8iXGL8P7Y8y4IejUvh98vDkQmZllplySHD09PWVAXp1SpexhaGiI0FAec7QhOjoaV31uom6iXFK3fk14JvhSJSFPTx/Ura96w7l6anLJkGH9MHrsYHTu0B9XU8glXbu3R2TEExw9cir9FSEiIkokFsJHGh9ZRbpmfDds2BDOzs5YvXo1KlWqBD09PVy+fBkDBw5Eo0aNUl3fyMgIRv+7kVq8+J+86YpFi1fh1/G/IOBOEO7cCcKv43/B27fvsGXrp2vRuaxZiEePQjFx0hwAwOLFq3HyxC6MHTMYrvuPoE3rpmjYsDbq1munrLN40R/o2sUJ7Tv0xatXr5XZNC9evML79++RM6cJpk4ejd17DiI07DGK2BXCzBm/IjLyGfbuPfR1X4Rv2OJFq7Bq9d/w9rqGixe90LdvNxQqlB+rVm0CAPz++zjkz28DZ+e4ywWsWrURAwf1wpw5k+DisgXVqlVE794/ok/vT9fLX7Z0DdyPbseoUYPg5nYUrVo1Rv36tdCoUSeN92tiYoxx44figNsxhIWFw8oqF5wH9ESBAvmwZ/cBZTs9e3bC7dt3EBn5BNWqVcTceVOxZPFqBATwLuIZbfGS1Rg3bigC7tzDnTtBGD9+KN6+fY+tW/cqZVavXoBHj8IwefKfAIAlS1fj+LGdGD36Z7jtd0er1k3QoMEPqN+gvbLOwkUr4bLmH1zxuoaLF66gX7/uKFSoAFau3KiUad++JSIjn+D+/UcoU7ok/po/Da6uR5QbZxYrZocuXZxw+PBJPHnyFI4lS+DPPyfD2/s6zp3z/DovEAEAli5djbFjh+DO//rJuHFD8e7de2zbtk8ps2rV33j0KAxTpsz93zouOHp0O0aPHoT9+4+idevGaNCgFho27Kiss2jRKqxevQBe/8sh/fp1VckhT58+TzJDLzo6Go8fRyj5wdQ0J9zcNsDY2Bg//TQc5uZmMDeP+3I7IuIJYmNjM/KloWQsW7IGy1fOg4/3DXhe8kbvPp1RoGA+uKzeAgCYPG008uWzweCB4wAALqu3oP+AHpg5ewLWr92OKlUroEevjnD+aZSyzV9GOOO3SSMwoO8ohAQ/gLV13CzQN2/e4s2btwCAnDlNULSYnbKOnV1BlCnriGfPnuPhg5R/sUYZT1u5xMTEGOPHD8WBA3HnI5aWuTFgQE8UKGCL3f87HylatDC6dHHCkSMnERn5DI6OJTBnziR4e99IcSIJZawVS12w9P/m4ur/ckmvPp1RsGA+rF2zFQAwaeoo2OazwdBB4wEA69ZsRT/n7pg+61dsWBeXS7r37ICB/T5dMmvo8P74deJwDOo/GvdDHqrNJUDc58Wu3dtj25a9vPwe0Tfu7dt3CHnwSPn74aPHuO1/FxbmZir3JyIiStfA95o1a9C7d29UrVpVuSZtdHQ0mjVrhpUrV37RALOqeX8tg7FxDixZ9Ady57bApUveaN6yG16/fqOUKVwov8oAwPkLl9Gtx2BM/30cfp82FncDg9G1+8+45PlpBsXPg3oDAE4cV72Led9+I7F+w3Z8/BiLMmVKokePjsiVyxyhoeE4dfocunb/WWXflLF27XKDpVUu/DphOGxt8+LWLX+0b/cT7t9/CCDuhlIFC326cVRw8AO0b/cT/pw7GQMG9kRoaDjGjPkd+/YdVspcvOiF3r1+wZSpYzB5yigEBoagV6+huOzpo/F+P36Mhb39d+i+pQOsrHLj6dPnuHLlGho37gRf3wBlOyVKFMPv08chd24LBAc/wLy5S7B48eoMftUIAObPXw5j4xxYtHBmXO7w9EHLVt1V3r+FChVAbOynb1gvXLiCHj2H4PdpYzFt6hgEBgaje4/B8EzQN3bu3A8ry9z47bfhyGdrjZs3/dDWqTdCQh4qZfLZWmPu3Cmwsc6D0LBwbNq0C3/8sVB5PioqCvXr/4ChQ/rB1NQEDx6E4tCh45g56x8OZn5l8+evQI4cOfDPPzORO7c5PD190KpVj0T9RPUYc+HCFfTq9QumTh2NKVNGIzAwBD17Dk3UT9xgaZkbv/02DLa21rh50x9OTn1U+klqKlQoi6pVKwIAbt3yUHnOwaEWQkIepLPW9Dn27D6I3Ja5MHb8ENjYWsP3lj86d3TGg/txHxptbK1RsFB+pXxI8AN07uCMWXN+Qz/nHggLfYxfx87EftcjSpl+/bvByMgQ6zYtUdnXn38swp+zFwMAvq9QBvsPbVKemzVnIgBg86bdysAYaY+2csnHj7FwcCiOHj06Kucjly9fRaNGn85HoqOjUb9+LQwZ0lc55hw+fAKzeMzRqr27DyG3ZW6MHjcYNrbWuO3rj66dBnzKJTZ5UbBgPqV8SPADdOs0ADNmT0Bf5+4ICwvHb+Nnwc3106/JfurXFUZGhnDZsFhlX3NnL8a8OZ/yS936NVGocAFs2qD6OYiIvj03bgeg7y+fziPmLv4XANC2eSPMmqTZvWiI6NugJ59xV8mAgAD4+vpCRODo6Ah7e/t0B6JvmPzd44niGekbaDsEyuRiYjkDiFKXTS9dV/qib4yxfvKXXCCK9y6GN/Ok1Jka5tB2CJTJRbzgPRIoddGR/AUupc4gTzFth5Dl5LVwSL0Qqcgqxy2NZ3yPGjUKM2bMQM6cOTFq1Kgkz58+fVr5/7///vvLREdERERERERERERElEYaD3x7e3srN7T09lZ/8xJA967VTURERERERERERERZi8YD3ydPnlT7/0RERERERERERERZ0WdcBZoyOV7klIiIiIiIiIiIiIh0Cge+iYiIiIiIiIiIiEincOCbiIiIiIiIiIiIiHQKB76JiIiIiIiIiIiISKdw4JuIiIiIiIiIiIiIdIq+tgMgIiIiIiIiIiIi0oZYEW2HQBmEM76JiIiIiIiIiIiISKdw4JuIiIiIiIiIiIiIdAoHvomIiIiIiIiIiIhIp3Dgm4iIiIiIiIiIiIh0Cge+iYiIiIiIiIiIiEin6Gs7ACIiIiIiIiIiIiJtEBFth0AZhDO+iYiIiIiIiIiIiEincOCbiIiIiIiIiIiIiHQKB76JiIiIiIiIiIiISKdw4JuIiIiIiIiIiIiIdAoHvomIiIiIiIiIiIhIp+hrOwAiIiIiIiIiIiIibYiFaDsEyiCc8U1EREREREREREREOoUD30RERERERERERESkUzjwTUREREREREREREQ6hQPfRERERERERERERKRTOPBNRERERERERERERDpFX9sBEBEREREREREREWmDiGg7BMognPFNRERERERERERERDqFA99EREREREREREREpFM48E1EREREREREREREOoUD30RERERERERERESkUzjwTUREREREREREREQ6RV/bARARERERERERERFpQ6yItkOgDMIZ30RERERERERERESkUzjwTUREREREREREREQ6hQPfRERERERERERERKRTOPBNRERERERERERERDqFA99EREREREREREREpFM48E1ERERERERERETfJOG/NP9Lj2XLlqFo0aLIkSMHKlWqBA8PjxTLnz59GpUqVUKOHDlQrFgxrFixIs375MA3EREREREREREREWWIbdu2YcSIEZg4cSK8vb1Ru3ZtNG/eHCEhIWrLBwUFoUWLFqhduza8vb3x22+/YdiwYdi1a1ea9qsnIukbpv/C9A0LaDsEygKM9A20HQJlcjGxH7UdAmUB2fT4vS+lzljfUNshUBbwLiZK2yFQFmBqmEPbIVAmF/HCT9shUBYQHRmo7RAoCzDIU0zbIWQ5OU2KaDuELOfN23tpKl+tWjVUrFgRy5cvV5Y5OjrCyckJs2fPTlJ+/PjxcHV1ha+vr7Js0KBBuHr1Ks6fP6/xfvnJn4iIiIiIiIiIiIg08uHDB7x8+VLl8eHDB7Vlo6KicOXKFTRp0kRleZMmTXDu3Dm165w/fz5J+aZNm+Ly5cuIjo7WOE4OfBMRERERERERERGRRmbPng0LCwuVh7qZ2wAQGRmJjx8/wsbGRmW5jY0NwsLC1K4TFhamtnxMTAwiIyM1jlNf45JERERERERERERE9E2bMGECRo0apbLMyMgoxXX09PRU/haRJMtSK69ueUo48E1ERERERERERETfpNjMcfvDLMXIyCjVge54efLkQfbs2ZPM7g4PD08yqzuera2t2vL6+vqwsrLSOE5e6oSIiIiIiIiIiIiIvjhDQ0NUqlQJR48eVVl+9OhR1KxZU+06NWrUSFLe3d0dlStXhoGBgcb75sA3EREREREREREREWWIUaNGYdWqVVizZg18fX0xcuRIhISEYNCgQQDiLp3Sq1cvpfygQYMQHByMUaNGwdfXF2vWrMHq1asxZsyYNO2XlzohIiIiIiIiIiIiogzRuXNnPHnyBNOnT0doaCjKlCmDgwcPws7ODgAQGhqKkJAQpXzRokVx8OBBjBw5EkuXLkX+/PmxaNEidOjQIU371RPJHBey0TcsoO0QKAsw0tf85wz0bYqJ/ajtECgLyKbHHzxR6oz1DbUdAmUB72KitB0CZQGmhjm0HQJlchEv/LQdAmUB0ZGB2g6BsgCDPMW0HUKWY2xsp+0Qspx374K1HYJG+MmfiIiIiIiIiIiIiHQKL3VCRERERERERERE36RMcjEMygCc8U1EREREREREREREOoUD30RERERERERERESkUzjwTUREREREREREREQ6hQPfRERERERERERERKRTOPBNRERERERERERERDpFX9sBEBEREREREREREWmDQLQdAmUQzvgmIiIiIiIiIiIiIp3CgW8iIiIiIiIiIiIi0ikc+CYiIiIiIiIiIiIincKBbyIiIiIiIiIiIiLSKRz4JiIiIiIiIiIiIiKdoq/tAIiIiIiIiIiIiIi0QUS0HQJlEM74JiIiIiIiIiIiIiKdwoFvIiIiIiIiIiIiItIpHPgmIiIiIiIiIiIiIp3CgW8iIiIiIiIiIiIi0ikc+CYiIiIiIiIiIiIinaKv7QCIiIiIiIiIiIiItEFEtB0CZRDO+CYiIiIiIiIiIiIincKBbyIiIiIiIiIiIiLSKRz4JiIiIiIiIiIiIiKdwoFvIiIiIiIiIiIiItIpHPgmIiIiIiIiIiIiIp2ir+0AiIiIiIiIiIiIiLRBtB0AZRjO+CYiIiIiIiIiIiIincKBbyIiIiIiIiIiIiLSKRz4JiIiIiIiIiIiIiKdwoFvIiIiIiIiIiIiItIpHPgmIiIiIiIiIiIiIp2iJyK8eWkm9OHDB8yePRsTJkyAkZGRtsOhTIr9hFLDPkKaYD8hTbCfkCbYTyg17COkCfYT0gT7CRGlhgPfmdTLly9hYWGBFy9ewNzcXNvhUCbFfkKpYR8hTbCfkCbYT0gT7CeUGvYR0gT7CWmC/YSIUsNLnRARERERERERERGRTuHANxERERERERERERHpFA58ExEREREREREREZFO4cB3JmVkZISpU6fyBg2UIvYTSg37CGmC/YQ0wX5CmmA/odSwj5Am2E9IE+wnRJQa3tySiIiIiIiIiIiIiHQKZ3wTERERERERERERkU7hwDcRERERERERERER6RQOfBMRERERERERERGRTuHAt47o06cPnJyctB0GaahevXoYMWJEmte7d+8e9PT04OPjo/E6a9euRa5cudK8L/qyTp06BT09PTx//lzboWS4rNrnEr4vixQpgn/++Uer8WS0t2/fokOHDjA3N/9m+mZK9PT0sHfvXm2H8Vm+pTxDRFlb4s8uic+N03IczqrnHUSUdql9jv4WzuGJKG048P2ZMsuA88KFC7F27Vpth5GlZZa2TEmhQoUQGhqKMmXKfNHtZoW6U9bRuXNn+Pv7azuMz+Lp6YkBAwZoVDarnmCvW7cOHh4eOHfuHEJDQ2FhYaHtkL6KadOm4fvvv9d2GN8EDsRnHjzOU0ZJ72SO1KTlOJz4vIN5XjsyU87PqH5J2rd7927MmDFD22EQURair+0AKE50dDQMDAzSvf63MmCRFXxuWyYnKioKhoaGsLW1/eLbJhIRfPz4Efr6n39YMDY2hrGx8ReISnvy5s2r7RAy3N27d+Ho6JjiF2nxeUcXxPdxIkq/jDrHIUosLcdhXTjvICLNWFpaajsEIspiOONbQzt37kTZsmVhbGwMKysrNGrUCGPHjsW6deuwb98+6OnpQU9PD6dOnQIAjB8/Hvb29jAxMUGxYsUwefJkREdHK9uLn4mwZs0aFCtWDEZGRhCRNMfw5s0bAKozeeIvh5H4Ua9ePWVb586dQ506dWBsbIxChQph2LBhyrZ0XWZoSwCIiYnB0KFDkStXLlhZWWHSpEkq6xUpUgQzZ85Enz59YGFhAWdnZ7WXOnF1dUWJEiVgbGyM+vXrY926dWpnWxw5cgSOjo4wNTVFs2bNEBoaqsSvru7x+9q9ezfq168PExMTlC9fHufPn1fZbmp9admyZShRogRy5MgBGxsbdOzYMcW2yKz98NWrV+jevTty5syJfPnyYcGCBSqzSTZu3IjKlSvDzMwMtra26NatG8LDw5Pd3pMnT9C1a1cULFgQJiYmKFu2LLZs2aI8HxERAVtbW/zxxx/KsosXL8LQ0BDu7u64d+8esmXLhsuXL6tsd/HixbCzs0u1D8bPyjly5AgqV64MIyMjeHh4QEQwd+5cFCtWDMbGxihfvjx27typsm5qfU7dT46XL1+O7777DoaGhnBwcMCGDRtUntfT08OqVavQrl07mJiYoESJEnB1dU2xDp/jzZs36NWrF0xNTZEvXz7Mnz9f5fnEs7inTZuGwoULw8jICPnz58ewYcMAxM0oCg4OxsiRI5X3D5B6+8avO2zYMIwbNw6WlpawtbXFtGnTVMo8f/4cAwYMgI2NDXLkyIEyZcrAzc1NeT69ubxevXqYP38+zpw5o3J8UJd3AGDXrl0oXbo0jIyMUKRIEbWv18yZM5XX1M7ODvv27UNERATatm0LU1NTlC1bNkl/TUlqdUvtPaeuj2/YsAG///47rl69qrRXwl9LRUZGfrU+qI4u5Zl79+6hfv36AIDcuXNDT08Pffr0wfr162FlZYUPHz6olO/QoQN69eoF4NNx9f/+7/9QqFAhmJiYoFOnTkmOay4uLnB0dESOHDlQsmRJLFu2LPkX9xuRGc5x6tWrh19++QUjRoxA7ty5YWNjg3///Rdv3rzBTz/9BDMzM3z33Xc4dOiQss7Hjx/Rr18/FC1aFMbGxnBwcMDChQtVtnvq1ClUrVoVOXPmRK5cuVCrVi0EBwcDAK5evYr69evDzMwM5ubmqFSpUpryzbciPW1z69YttGjRAqamprCxsUHPnj0RGRkJIO7zx+nTp7Fw4UKlb927d0+j9kxN4uNwSsfDhOcda9euVZvn+/bti1atWqnsIyYmBra2tlizZk06Xs2sTV2uOH36NAwMDBAWFqZSdvTo0ahTpw4AIDg4GK1bt0bu3LmRM2dOlC5dGgcPHkw25wNI9dwy4fG6QoUKMDY2RoMGDRAeHo5Dhw7B0dER5ubm6Nq1K96+fZtq3dT1y6CgIBQvXhx//fWXStkbN24gW7ZsuHv3LoC489Hly5ejefPmMDY2RtGiRbFjxw6VdR4+fIjOnTsjd+7csLKyQtu2bXHv3r00vf6UfgnPi8LDw9G6dWulrTZt2qTd4IgocxJK1aNHj0RfX1/+/vtvCQoKkmvXrsnSpUvl1atX8uOPP0qzZs0kNDRUQkND5cOHDyIiMmPGDDl79qwEBQWJq6ur2NjYyJ9//qlsc+rUqZIzZ05p2rSpeHl5ydWrVyU2NjZdMYiI9O7dW9q2bSsiIjExMUo8oaGh4u3tLVZWVjJ58mQREbl27ZqYmprKggULxN/fX86ePSsVKlSQPn36ZNArmHlkhrYUEalbt66YmprK8OHD5fbt27Jx40YxMTGRf//9VyljZ2cn5ubmMm/ePAkICJCAgAAJCgoSAOLt7S0iIkFBQWJgYCBjxoyR27dvy5YtW6RAgQICQJ49eyYiIi4uLmJgYCCNGjUST09PuXLlijg6Okq3bt1ERJKte/y+SpYsKW5ubuLn5ycdO3YUOzs7iY6OFpHU+5Knp6dkz55dNm/eLPfu3RMvLy9ZuHBhqm2RGfXv31/s7Ozk2LFjcv36dWnXrp2YmZnJ8OHDRURk9erVcvDgQbl7966cP39eqlevLs2bN1fWP3nypEq7PHjwQObNmyfe3t5y9+5dWbRokWTPnl0uXLigrHPgwAExMDAQT09PefXqlRQvXlzZn4hI48aNZfDgwSpxVqhQQaZMmZJqfeLjKVeunLi7u8udO3ckMjJSfvvtNylZsqQcPnxY7t69Ky4uLmJkZCSnTp0SEc37nIWFhbKv3bt3i4GBgSxdulT8/Pxk/vz5kj17djlx4oRSBoAULFhQNm/eLAEBATJs2DAxNTWVJ0+eaNI8afbzzz9LwYIFxd3dXa5duyatWrVS3pMice+/BQsWiIjIjh07xNzcXA4ePCjBwcFy8eJF5b365MkTKViwoEyfPl15/4ho1r5169YVc3NzmTZtmvj7+8u6detET09P3N3dRUTk48ePUr16dSldurS4u7vL3bt3Zf/+/XLw4EER+bxc/uTJE3F2dpYaNWpIaGio8jqryzuXL1+WbNmyyfTp08XPz09cXFzE2NhYXFxclO3Z2dmJpaWlrFixQvz9/eXnn38WMzMzadasmWzfvl38/PzEyclJHB0dU82PmtZN0/dcwj7+4MEDGT16tJQuXVppr7dv34rI1++D6uhSnomJiZFdu3YJAPHz85PQ0FB5/vy5vH37ViwsLGT79u1K2YiICDE0NFRyQvxxtUGDBuLt7S2nT5+W4sWLK8ctEZF///1X8uXLJ7t27ZLAwEDZtWuXWFpaytq1a9P0muuSzHSOY2ZmJjNmzBB/f3+ZMWOGZMuWTZo3by7//vuvkiOsrKzkzZs3IiISFRUlU6ZMkUuXLklgYKByXrRt2zYREYmOjhYLCwsZM2aM3LlzR27duiVr166V4OBgEREpXbq09OjRQ3x9fcXf31+2b98uPj4+GfEyZ2lpbZtHjx5Jnjx5ZMKECeLr6yteXl7SuHFjqV+/voiIPH/+XGrUqCHOzs5K34qJiUm1PUVUP7vEx5Yw9yQ8Dqd2PEx43vH27Vu1ef7s2bOSPXt2efTokbKPffv2Sc6cOTPtuWdGSSlX2Nvby9y5c5Wy0dHRYm1tLWvWrBERkZYtW0rjxo3l2rVrSjucPn062ZwvIqmeW8Yfu6pXry7//fefeHl5SfHixaVu3brSpEkT8fLykjNnzoiVlZXMmTMn1fol1y9nzZolpUqVUik7cuRIqVOnjvI3ALGyspKVK1eKn5+fTJo0SbJnzy63bt0SEZE3b95IiRIlpG/fvnLt2jW5deuWdOvWTRwcHJS8ShkrYa5o3ry5lClTRs6dOyeXL1+WmjVrirGxsZI7iIhERDjwrYErV64IALl3716S5xKftCVn7ty5UqlSJeXvqVOnioGBgYSHh392DCnF8e7dO6lWrZq0atVKPn78KCIiPXv2lAEDBqiU8/DwkGzZssm7d+80iierygxtKRJ3wE48ADR+/HhxdHRU/razsxMnJyeV9RIPfI8fP17KlCmjUmbixIlJBiEByJ07d5QyS5cuFRsbG+VvdXWP39eqVauUZTdv3hQA4uvrKyKp96Vdu3aJubm5vHz5MslrkFqfzkxevnwpBgYGsmPHDmXZ8+fPxcTEROVDWkKXLl0SAMqHqcQDUuq0aNFCRo8erbJs8ODBYm9vL927d5cyZcqovEe3bdsmuXPnlvfv34uIiI+Pj+jp6UlQUFCqdYqPZ+/evcqy169fS44cOeTcuXMqZfv16yddu3YVEc37XMKB75o1a4qzs7PKOp06dZIWLVoofwOQSZMmqcSip6cnhw4dSrUuafXq1SsxNDSUrVu3KsuePHkixsbGage+58+fL/b29hIVFaV2ewnLpiRx+9atW1d++OEHlTJVqlSR8ePHi4jIkSNHJFu2bOLn56d2e5+by4cPHy5169ZNUpfEeadbt27SuHFjlWVjx45V+fBoZ2cnPXr0UP4ODQ0VAMoXriIi58+fFwDKlwMpSU/dknvPJezjInE5u3z58knW/5p9UB1dzjOJ4/n5559VBuz/+ecfKVasmHJMnDp1qmTPnl3u37+vlDl06JBky5ZN6T+FChWSzZs3q2x3xowZUqNGjVTj0lWZ6RwnYW6LiYmRnDlzSs+ePZVl8Tni/PnzyW5n8ODB0qFDBxGJy9EAlIGyxMzMzL7pLz00lda2mTx5sjRp0kRlG/fv31cGN+O3mVyOSihhe4qkbeA7teNh4vOO5PJ8qVKlVL7YcXJy+iYm/iSWUq74888/VT6P7N27V0xNTeX169ciIlK2bFmZNm2a2u2qy/manFvGr3fs2DHl+dmzZwsAuXv3rrJs4MCB0rRpU43qqK5fPnr0SLJnzy4XL14Ukbgv3PLmzauSOwDIoEGDVNarVq2a/PzzzyIS9wW0g4ODyme4Dx8+iLGxsRw5ckSj2OjzxLetn5+fAFD5Mt/X11cAcOCbiFTwUicaKF++PBo2bIiyZcuiU6dOWLlyJZ49e5biOjt37sQPP/wAW1tbmJqaYvLkyQgJCVEpY2dnp/H169ITAwD069cPr169wubNm5EtW1xzX7lyBWvXroWpqanyaNq0KWJjYxEUFKRRPFlVZmjLeNWrV1cuiwAANWrUQEBAgMo1aCtXrpziNvz8/FClShWVZVWrVk1SzsTEBN99953yd758+VL8eXxC5cqVU1kPgLJuan2pcePGsLOzQ7FixdCzZ09s2rRJ+Ylievu0NgQGBiI6OlrltbWwsICDg4Pyt7e3N9q2bQs7OzuYmZkpl45I3Ffiffz4EbNmzUK5cuVgZWUFU1NTuLu7Jyn/119/ISYmBtu3b8emTZuQI0cO5TknJyfo6+tjz549AIA1a9agfv36KFKkiMZ1S9jHbt26hffv36Nx48Yqbbp+/XrlJ6Ca9rmEfH19UatWLZVltWrVgq+vr8qyhH0tZ86cMDMz07ifpsXdu3cRFRWFGjVqKMssLS1V2jOhTp064d27dyhWrBicnZ2xZ88exMTEpLgPTds3YZ0B1femj48PChYsCHt7e7X7yKhcnjjvJNd+ifNVwrrY2NgAAMqWLZtkmSZtqkndNH3PpZZHE/pafVAdXc4ziTk7O8Pd3R0PHz4EEHfJkj59+qgcEwsXLoyCBQsqf9eoUQOxsbHw8/NDREQE7t+/j379+qn0kZkzZyq56luUmc5xEr6XsmfPDisrq1TzwYoVK1C5cmXkzZsXpqamWLlypRKLpaUl+vTpg6ZNm6J169ZYuHChcsk2ABg1ahT69++PRo0aYc6cOd90P0hNWtrmypUrOHnypMr7rGTJkgCQ6mucUnumVWrHQ031798fLi4uAOLqd+DAAfTt2/eztpkVpZQr+vTpgzt37uDChQsA4nL+jz/+iJw5cwIAhg0bhpkzZ6JWrVqYOnUqrl27luK+NDm3jJf4PCL+EkwJl33OMTlfvnxo2bKlcmkbNzc3vH//Hp06dVIpl/D8MP7v+HPWK1eu4M6dOzAzM1PqYmlpiffv3zPvfGW+vr7Q19dXOc8rWbJkksstEhFx4FsD2bNnx9GjR3Ho0CGUKlUKixcvhoODQ7IDCxcuXECXLl3QvHlzuLm5wdvbGxMnTkRUVJRKufgTiIyIAQBmzpyJw4cPw9XVFWZmZsry2NhYDBw4ED4+Psrj6tWrCAgIUBkc1UWZoS3TIrXtiojKQEH8ssQS34hKT09Po+uQJ143fl+xsbHKf1PqS2ZmZvDy8sKWLVuQL18+TJkyBeXLl8fz58/T1ae1Jf61Su61fvPmDZo0aQJTU1Ns3LgRnp6eyiBR4r4Sb/78+ViwYAHGjRuHEydOwMfHB02bNk1SPjAwEI8ePUJsbKxyLdN4hoaG6NmzJ1xcXBAVFYXNmzen+QNcwj4W364HDhxQadNbt24p12LUtM8lpm6dxMvU9dP4mL4kTft+vEKFCsHPzw9Lly6FsbExBg8ejDp16qhcBzcxTds3pTqndqOujMrlifNOevJMfPmU8kdKUqtbWt5zacnPX6sPqqPLeSaxChUqoHz58li/fj28vLxw/fp15VqwyYl/XRK2ycqVK1X6yI0bN5TBmm9RZjrHUfdeSikfbN++HSNHjkTfvn3h7u4OHx8f/PTTTyqxuLi44Pz586hZsya2bdsGe3t7pb2nTZuGmzdvomXLljhx4gRKlSqlvD9IVVraJjY2Fq1bt1Z5n/n4+CAgIEC55rM6mrRnWnypG1f26tULgYGBOH/+PDZu3IgiRYqgdu3aX2TbWUlKucLa2hqtW7eGi4sLwsPDcfDgQZWc379/fwQGBqJnz564fv06KleujMWLFye7L03OLeMl7ocZcUzu378/tm7dinfv3sHFxQWdO3eGiYlJquslfF9UqlQpyXvC398f3bp1+6zYKG2SO28iIkpMX9sBZBV6enqoVasWatWqhSlTpsDOzg579uyBoaGhyow3ADh79izs7OwwceJEZVniD5JfMoZRo0YlKbtr1y5Mnz4dhw4dSjIAUrFiRdy8eRPFixf/7JiyoszQlgCSfDi/cOECSpQogezZs2u8jZIlS+LgwYMqy9JzMyd1ddeEJn1JX18fjRo1QqNGjTB16lTkypULJ06cQPv27dPUp7Xpu+++g4GBAS5duoRChQoBAF6+fImAgADUrVsXt2/fRmRkJObMmaM8n1o7eHh4oG3btujRoweAuBPpgIAAODo6KmWioqLQvXt3dO7cGSVLlkS/fv1w/fp1ZTYWEHcCX6ZMGSxbtgzR0dFo3759uutZqlQpGBkZISQkBHXr1lVbJj19ztHREf/9959y4zog7saFCev6NRUvXhwGBga4cOECChcuDAB49uwZ/P39k623sbEx2rRpgzZt2mDIkCEoWbIkrl+/jooVK6p9/2jSvqkpV64cHjx4AH9/f7Wz3L5WLi9VqhT+++8/lWXnzp2Dvb19mvJVWqRWt+vXr6f5PRcvvfkuo+linjE0NAQAta93//79sWDBAjx8+BCNGjVS6hQvJCQEjx49Qv78+QEA58+fR7Zs2WBvbw8bGxsUKFAAgYGB6N69u0axfCsyyzlOWnl4eKBmzZoYPHiwskzd7MkKFSqgQoUKmDBhAmrUqIHNmzejevXqAAB7e3vY29tj5MiR6Nq1K1xcXNCuXbuvVgddVLFiRezatQtFihSBvr76j43JHQM1aU9NpXY81CQmALCysoKTk5PyJcpPP/2U7piyupTOwfv3748uXbqgYMGC+O6775L86qtQoUIYNGgQBg0ahAkTJmDlypX45Zdf1OZ8Tc4tM0JyfaBFixbImTMnli9fjkOHDuHMmTNJyly4cEHlnPXChQuoUKECgLj3xLZt22BtbQ1zc/OMqwClytHRETExMbh8+bLyazk/P78kN8ImIuKMbw1cvHgRf/zxBy5fvoyQkBDs3r0bERERcHR0RJEiRXDt2jX4+fkhMjIS0dHRKF68OEJCQrB161bcvXsXixYt+uxZJynFkNiNGzfQq1cvjB8/HqVLl0ZYWBjCwsLw9OlTAMD48eNx/vx5DBkyRJm14erqil9++eWzYswKMkNbxrt//z5GjRoFPz8/bNmyBYsXL8bw4cPTtI2BAwfi9u3bGD9+PPz9/bF9+3asXbsWQNq+/VZXd02k1pfc3NywaNEi+Pj4IDg4GOvXr0dsbCwcHBzS1Ke1zczMDL1798bYsWNx8uRJ3Lx5E3379kW2bNmgp6eHwoULw9DQEIsXL0ZgYCBcXV0xY8aMFLdZvHhxHD16FOfOnYOvry8GDhyIsLAwlTITJ07EixcvsGjRIowbNw6Ojo7o16+fShlHR0dUr14d48ePR9euXT9rVpSZmRnGjBmDkSNHYt26dbh79y68vb2xdOlSrFu3DkD6+tzYsWOxdu1arFixAgEBAfj777+xe/dujBkzJt2xfg5TU1P069cPY8eOxfHjx3Hjxg306dNHuRxUYmvXrsXq1atx48YNBAYGYsOGDTA2NoadnR2AuPfPmTNn8PDhQ0RGRgLQrH1TU7duXdSpUwcdOnTA0aNHERQUhEOHDuHw4cMAvl4uHz16NI4fP44ZM2bA398f69atw5IlSzK0/VKrW3rec/GKFCmCoKAg+Pj4IDIyEh8+fMiweqSFLuYZOzs76Onpwc3NDREREXj9+rXyXPfu3fHw4UOsXLlS7QzyHDlyoHfv3rh69So8PDwwbNgw/Pjjj7C1tQUQN8N39uzZWLhwIfz9/XH9+nW4uLjg77//1ig2XZSZznHSqnjx4rh8+TKOHDkCf39/TJ48GZ6ensrzQUFBmDBhAs6fP4/g4GC4u7vD398fjo6OePfuHYYOHYpTp04hODgYZ8+ehaenZ6Y8n8hqhgwZgqdPn6Jr1664dOkSAgMD4e7ujr59+yqDikWKFMHFixdx7949REZGIjY2NtX2TKvUjoeJpZTn+/fvj3Xr1sHX1xe9e/dOd0xZWWrn4E2bNoWFhQVmzpyZ5MuBESNG4MiRIwgKCoKXlxdOnDihrKcu52tybpkR1PVLIG62e58+fTBhwgQUL148yWVNAGDHjh1Ys2YN/P39MXXqVFy6dAlDhw4FEHfsypMnD9q2bQsPDw8EBQXh9OnTGD58OB48eJBh9aGkHBwc0KxZMzg7O+PixYu4cuUK+vfv/8V+IUJEOkQbFxbPam7duiVNmzaVvHnzipGRkdjb28vixYtFRCQ8PFwaN24spqamAkBOnjwpInE3/rKyshJTU1Pp3LmzLFiwQKObrqQnBhHVG8TE38ww8SPhTcwuXbqkxJ0zZ04pV66czJo1K70vUZaRGdpSJO6mHIMHD5ZBgwaJubm55M6dW3799VeVG6Wou2Fe4ptbisTdkb548eJiZGQk9erVk+XLlwsA5eZkiW/4IyKyZ88eSfj2V1d3dft69uyZymsjknJf8vDwkLp160ru3LnF2NhYypUrJ9u2bROR1Pt0ZvPy5Uvp1q2bmJiYiK2trfz9999StWpV+fXXX0VEZPPmzVKkSBExMjKSGjVqiKurq8rrl/iGP0+ePJG2bduKqampWFtby6RJk6RXr17K+/jkyZOir68vHh4eSgzBwcFiYWEhy5YtU4lt9erVAkAuXbqkcX2Su+lcbGysLFy4UBwcHMTAwEDy5s0rTZs2ldOnTytl0tPnli1bJsWKFRMDAwOxt7eX9evXqzwPQPbs2aOyzMLCQlxcXDSuU1q8evVKevToISYmJmJjYyNz585VuRFSwvffnj17pFq1amJubi45c+aU6tWrq9yA6fz581KuXDkxMjJS3lepta+I+hsvtW3bVnr37q38/eTJE/npp5/EyspKcuTIIWXKlBE3Nzfl+c/J5cnd3FLdDYF27twppUqVEgMDAylcuLDMmzcv1fUSt6m6nJKS1OqW1vdcvPfv30uHDh0kV65cAkDpY1+7D6qja3lGRGT69Olia2srenp6Kn1bJO4mppaWlsqNM+PFH1eXLVsm+fPnlxw5ckj79u3l6dOnKuU2bdok33//vRgaGkru3LmlTp06snv37jTFp0sy0zlO4tyWWo54//699OnTRywsLCRXrlzy888/y6+//qrsOywsTJycnCRfvnxiaGgodnZ2MmXKFPn48aN8+PBBunTpIoUKFRJDQ0PJnz+/DB06VOdv2J4e6Wkbf39/adeuneTKlUuMjY2lZMmSMmLECOWc1c/PT6pXry7GxsYCQIKCglJtT5G03dxSJOXjYeLzjuTyvEjceY6dnZ3KDba/NZqcg0+ePFmyZ88ujx49Ulk+dOhQ+e6778TIyEjy5s0rPXv2lMjISOV5dTk/tXNLdcdrdeeSaclH6vplvLt37woAmTt3bpL1AMjSpUulcePGYmRkJHZ2drJlyxaVMqGhodKrVy/JkyePGBkZSbFixcTZ2VlevHihUWz0eRLmitDQUGnZsqUYGRlJ4cKFZf369RrfdJ6Ivh16Imm82CkRZWqzZs3CihUrcP/+fW2HotPevHmDAgUKYP78+UlmR35ts2bNwtatW3H9+nWt7Z99jujL0/U807hxYzg6OmLRokUqy6dNm4a9e/fCx8fni+2LiAgA3r59i/z582PNmjWfdXk4Xefs7IzHjx/D1dVV26F8cWfPnkW9evXw4MEDlct6AXG/XtyzZw+cnJy0ExwREX1xvMY3URa3bNkyVKlSBVZWVjh79izmzZun/ByPvhxvb2/cvn0bVatWxYsXLzB9+nQAQNu2bbUW0+vXr+Hr64vFixdrfJmHL4F9jihjfCt55unTp3B3d8eJEyewZMmSL7JNIqKUxMbGIiwsDPPnz4eFhQXatGmj7ZAypRcvXsDT0xObNm3Cvn37tB3OF/Xhwwfcv38fkydPxo8//phk0JuIiHQTr/GdSYSEhMDU1DTZR0hIiLZDJA197bYMCAhA27ZtUapUKcyYMQOjR4/GtGnTvug+KM5ff/2F8uXLo1GjRnjz5g08PDyQJ08ercUzdOhQ/PDDD6hbt26Sa+QOGjQo2T44aNCgz9ov+1zm5+HhkWIe0rbmzZsnG9sff/yh7fC06lvIMxUrVsTAgQPx559/wsHBIaOrQF8Qz1cpqwoJCUGBAgWwfft2rFmzJtmbdX7r2rZtizZt2mDgwIFo3LixtsNRK715aMuWLXBwcMCLFy8wd+7crxw1ERFpCy91kknExMTg3r17yT6f0t3UKXNhW1JmEB4ejpcvX6p9ztzcHNbW1l85Ivqa3r17h4cPHyb7fPHixb9iNEk9fPgQ7969U/ucpaUlLC0tv3JElB7MM98enuMQkbYxDxERUVpw4JuIiIiIiIiIiIiIdAovdUJEREREREREREREOoUD30RERERERERERESkUzjwTUREREREREREREQ6hQPfRERERERERERERKRTOPBNRERERERERERERDqFA99EREREREREREREpFM48E1EREREREREREREOoUD30RERERERERERESkU/4f8t8fVXj3RIcAAAAASUVORK5CYII=",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(20,20))\n",
+ "sns.heatmap(df.corr(numeric_only=True), annot=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "0eba859f-3b26-4de0-bffb-5c4a3c375c5c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "system_type\n",
+ "0.0 1974\n",
+ "2.0 1585\n",
+ "1.0 1574\n",
+ "3.0 1322\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df['system_type'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "6ed2f797-f56e-4bb8-b829-d0f816d11227",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "star_spectral_class\n",
+ "G 1512\n",
+ "M 1401\n",
+ "A 1319\n",
+ "K 1246\n",
+ "F 1046\n",
+ "B 782\n",
+ "O 471\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df['star_spectral_class'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "1acba47c-6579-4f5d-af0a-13345fff9f48",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "planet_configuration\n",
+ "Rocky_Dominated 2422\n",
+ "Single_Planet 1973\n",
+ "Mixed_System 1318\n",
+ "Compact_System 1209\n",
+ "Gas_Giant_Dominated 855\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df['planet_configuration'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "b5446aac-d6ba-4e55-92bb-1491e6f5c8bb",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "stellar_activity_class\n",
+ "Low 3348\n",
+ "Medium 2763\n",
+ "High 1666\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df['stellar_activity_class'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "d9ffbfef-624a-4838-972f-c78bf1a02775",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df['system_type'].value_counts().plot(kind='bar')\n",
+ "plt.title('Target Distribution')\n",
+ "plt.xlabel('Class')\n",
+ "plt.ylabel('Count')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f977017e-0f53-48f9-a4d3-96e064eef36c",
+ "metadata": {},
+ "source": [
+ "### Feature Engineering"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "5b86bedf-b3b7-4c36-8b6e-bd4652d57b74",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df['star_brightness']=df['star_brightness'].fillna(df['star_brightness'].median())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "c51d6390-1847-4add-ba95-64c695bd2681",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df['distance_from_earth']=df['distance_from_earth'].fillna(df['distance_from_earth'].median())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "dbf0cc60-319d-40de-a960-27876597cd3f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df['galaxy_region']=df['galaxy_region'].fillna(df['galaxy_region'].mode()[0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "c357d827-746b-4cc6-b12b-1cf19e1a2aa5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "star_size 0\n",
+ "star_brightness 0\n",
+ "galaxy_region 0\n",
+ "distance_from_earth 0\n",
+ "galaxy_type 0\n",
+ "star_spectral_class 0\n",
+ "planet_configuration 0\n",
+ "stellar_activity_class 0\n",
+ "star_mass 0\n",
+ "metallicity 0\n",
+ "system_type 1322\n",
+ "id 6455\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.isnull().sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "fcf36d07-00bb-4c00-93a5-46f530c4f06d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df['stellar_activity_class']=df['stellar_activity_class'].map({'Low': 0,'Medium': 1,'High': 2})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "2aa0775e-5917-4e53-9c91-5fcbd7de11ec",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "le_st=LabelEncoder()\n",
+ "le_pl=LabelEncoder()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "772da38c-4df3-49a1-ab19-3a29eea9861d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "LabelEncoder() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. \n",
+ "
\n",
+ "
\n",
+ " Parameters \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "LabelEncoder()"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "le_pl.fit(df['planet_configuration'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "3c2a7bf4-7e65-4316-b32f-a54e3215add9",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "df['planet_configuration']=le_pl.transform(df['planet_configuration'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "52630177-81fe-49a9-b0a2-aab3d9060268",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "LabelEncoder() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. \n",
+ "
\n",
+ "
\n",
+ " Parameters \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "LabelEncoder()"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "le_st.fit(df['star_spectral_class'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "c0eea989-48f6-4cc2-9224-0e70a49ddae6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df['star_spectral_class']=le_st.transform(df['star_spectral_class'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "fc54abb5-a375-43c9-b46f-c35381cc2e5d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['star_encoder.pkl']"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "joblib.dump(le_pl, 'planet_encoder.pkl')\n",
+ "joblib.dump(le_st, 'star_encoder.pkl')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "id": "e7b9880a-8f79-423f-a244-f47236735169",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Index: 7777 entries, 0 to 1321\n",
+ "Data columns (total 12 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 star_size 7777 non-null float64\n",
+ " 1 star_brightness 7777 non-null float64\n",
+ " 2 galaxy_region 7777 non-null float64\n",
+ " 3 distance_from_earth 7777 non-null float64\n",
+ " 4 galaxy_type 7777 non-null int64 \n",
+ " 5 star_spectral_class 7777 non-null int64 \n",
+ " 6 planet_configuration 7777 non-null int64 \n",
+ " 7 stellar_activity_class 7777 non-null int64 \n",
+ " 8 star_mass 7777 non-null float64\n",
+ " 9 metallicity 7777 non-null float64\n",
+ " 10 system_type 6455 non-null float64\n",
+ " 11 id 1322 non-null float64\n",
+ "dtypes: float64(8), int64(4)\n",
+ "memory usage: 789.9 KB\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.info()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "403f3b7e-64bb-4b68-af76-591c2e4d85fe",
+ "metadata": {},
+ "source": [
+ "### Modelling"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "id": "8a01ca10-245f-498f-bb89-3446162db1c5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train=df[:6455]\n",
+ "test=df[6455:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "id": "3b6f8b1b-86a3-4960-977a-88fe5c864c35",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x=train.drop(['system_type','id'],axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "id": "aeccdbbc-d029-40aa-b8d2-df77e02da8d6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y=train[['system_type']]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "id": "bb0b9923-e6b8-4b4f-a027-aa06eb403105",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_train,x_val, y_train, y_val=train_test_split(x,y, test_size=.20, random_state=42,stratify=y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2d252e49-ef5d-4c86-a2ef-578a51136ddc",
+ "metadata": {},
+ "source": [
+ "### 1th LogisticRegression"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "id": "a2e5c94b-b89f-4925-b923-27fc7d9d780b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lr=LogisticRegression()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "id": "501d2d12-d1cb-4bfd-9b85-473f723df008",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "LogisticRegression() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. \n",
+ "
\n",
+ "
\n",
+ " Parameters \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " penalty \n",
+ " 'l2' \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " dual \n",
+ " False \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " tol \n",
+ " 0.0001 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " C \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " fit_intercept \n",
+ " True \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " intercept_scaling \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " class_weight \n",
+ " None \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " random_state \n",
+ " None \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " solver \n",
+ " 'lbfgs' \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " max_iter \n",
+ " 100 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " multi_class \n",
+ " 'deprecated' \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " verbose \n",
+ " 0 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " warm_start \n",
+ " False \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " n_jobs \n",
+ " None \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " l1_ratio \n",
+ " None \n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "LogisticRegression()"
+ ]
+ },
+ "execution_count": 53,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "lr.fit(x_train,y_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "id": "478d4581-92fd-4973-afbf-929df9bfa55e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tahmin=lr.predict(x_val)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "id": "2cc029b6-0d3b-4e76-9adf-e118f061eb48",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 0.7079783113865221\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('Accuracy:', accuracy_score(y_val, tahmin))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "id": "10821aba-fe5a-47ae-bb6e-3ecf4065e24e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0.0 0.74 0.76 0.75 395\n",
+ " 1.0 0.70 0.70 0.70 315\n",
+ " 2.0 0.76 0.83 0.79 317\n",
+ " 3.0 0.59 0.49 0.54 264\n",
+ "\n",
+ " accuracy 0.71 1291\n",
+ " macro avg 0.70 0.70 0.69 1291\n",
+ "weighted avg 0.70 0.71 0.70 1291\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(classification_report(y_val, tahmin))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "id": "fb8df319-c354-4ef3-85ab-f994b7629059",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "cm=confusion_matrix(y_val, tahmin).astype(int)\n",
+ "\n",
+ "sns.heatmap(cm, annot=True, fmt='d')\n",
+ "plt.show();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6a207be4-bf39-4f4c-b00e-3fae58ca66cb",
+ "metadata": {},
+ "source": [
+ "### 2th RandomForestClassifier"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "id": "486c32ee-9545-4b9e-8c26-7ebb4fe319f4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rf=RandomForestClassifier()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "id": "11b7b137-2134-4a5a-9255-1518eee34b58",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "RandomForestClassifier() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. \n",
+ "
\n",
+ "
\n",
+ " Parameters \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " n_estimators \n",
+ " 100 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " criterion \n",
+ " 'gini' \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " max_depth \n",
+ " None \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " min_samples_split \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " min_samples_leaf \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " min_weight_fraction_leaf \n",
+ " 0.0 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " max_features \n",
+ " 'sqrt' \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " max_leaf_nodes \n",
+ " None \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " min_impurity_decrease \n",
+ " 0.0 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " bootstrap \n",
+ " True \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " oob_score \n",
+ " False \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " n_jobs \n",
+ " None \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " random_state \n",
+ " None \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " verbose \n",
+ " 0 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " warm_start \n",
+ " False \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " class_weight \n",
+ " None \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " ccp_alpha \n",
+ " 0.0 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " max_samples \n",
+ " None \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " monotonic_cst \n",
+ " None \n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "RandomForestClassifier()"
+ ]
+ },
+ "execution_count": 65,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rf.fit(x_train,y_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "id": "98e6a284-3d08-4150-b146-82d39f81c015",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tahmin2=rf.predict(x_val)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "id": "a84d6432-7850-4a04-9857-7b0eb825fcb5",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 0.8760650658404338\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('Accuracy:', accuracy_score(y_val, tahmin2))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "id": "334edb02-7d58-46fb-9721-fc0ae630368f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0.0 0.91 0.90 0.90 395\n",
+ " 1.0 0.83 0.85 0.84 315\n",
+ " 2.0 0.92 0.90 0.91 317\n",
+ " 3.0 0.84 0.85 0.84 264\n",
+ "\n",
+ " accuracy 0.88 1291\n",
+ " macro avg 0.87 0.87 0.87 1291\n",
+ "weighted avg 0.88 0.88 0.88 1291\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(classification_report(y_val, tahmin2))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "id": "f5f3eac6-650b-41b7-a52b-6b24045055a8",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "cm=confusion_matrix(y_val, tahmin2).astype(int)\n",
+ "\n",
+ "sns.heatmap(cm, annot=True, fmt='d')\n",
+ "plt.show();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "447d978a-9fbf-4f80-9c64-add6eab0eec7",
+ "metadata": {},
+ "source": [
+ "### 3th RandomForestClassifier"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "id": "4a3349b1-b3ed-4969-9334-9a6f385750c8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gb=GradientBoostingClassifier()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 81,
+ "id": "db2b42e2-f256-48b4-854b-d3e133216cfe",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "GradientBoostingClassifier() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. GradientBoostingClassifier
\n",
+ "
\n",
+ "
\n",
+ " Parameters \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " loss \n",
+ " 'log_loss' \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " learning_rate \n",
+ " 0.1 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " n_estimators \n",
+ " 100 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " subsample \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " criterion \n",
+ " 'friedman_mse' \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " min_samples_split \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " min_samples_leaf \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " min_weight_fraction_leaf \n",
+ " 0.0 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " max_depth \n",
+ " 3 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " min_impurity_decrease \n",
+ " 0.0 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " init \n",
+ " None \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " random_state \n",
+ " None \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " max_features \n",
+ " None \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " verbose \n",
+ " 0 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " max_leaf_nodes \n",
+ " None \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " warm_start \n",
+ " False \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " validation_fraction \n",
+ " 0.1 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " n_iter_no_change \n",
+ " None \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " tol \n",
+ " 0.0001 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " ccp_alpha \n",
+ " 0.0 \n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "GradientBoostingClassifier()"
+ ]
+ },
+ "execution_count": 81,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "gb.fit(x_train,y_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "id": "28949b6c-fc63-45a6-8d13-93d353170778",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tahmin3=gb.predict(x_val)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "id": "c8d9c506-2a73-455c-9e9f-642b497124bd",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 0.8814872192099148\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('Accuracy:', accuracy_score(y_val, tahmin3))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 84,
+ "id": "ea06b318-35ba-4b52-a3d6-202cdd0d83be",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0.0 0.89 0.91 0.90 395\n",
+ " 1.0 0.84 0.85 0.85 315\n",
+ " 2.0 0.93 0.89 0.91 317\n",
+ " 3.0 0.85 0.86 0.86 264\n",
+ "\n",
+ " accuracy 0.88 1291\n",
+ " macro avg 0.88 0.88 0.88 1291\n",
+ "weighted avg 0.88 0.88 0.88 1291\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(classification_report(y_val, tahmin3))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3bf8298f-8ccd-49cd-b4af-b60a182ef5ea",
+ "metadata": {},
+ "source": [
+ "### 4th lightgbm"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 111,
+ "id": "75a11b7a-a1e1-4a64-984f-358574d6d6f9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lgb=LGBMClassifier(n_estimators=300,learning_rate=0.05,max_depth=-1,objective='multiclass',num_class=4,random_state=42)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 112,
+ "id": "9c83436f-2bec-46e4-b00c-6fd6c72c0799",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000225 seconds.\n",
+ "You can set `force_col_wise=true` to remove the overhead.\n",
+ "[LightGBM] [Info] Total Bins 1296\n",
+ "[LightGBM] [Info] Number of data points in the train set: 5164, number of used features: 10\n",
+ "[LightGBM] [Info] Start training from score -1.184920\n",
+ "[LightGBM] [Info] Start training from score -1.411394\n",
+ "[LightGBM] [Info] Start training from score -1.404271\n",
+ "[LightGBM] [Info] Start training from score -1.585331\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "LGBMClassifier(learning_rate=0.05, n_estimators=300, num_class=4,\n",
+ " objective='multiclass', random_state=42) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. iFitted
\n",
+ "
\n",
+ "
\n",
+ " Parameters \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " boosting_type \n",
+ " 'gbdt' \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " num_leaves \n",
+ " 31 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " max_depth \n",
+ " -1 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " learning_rate \n",
+ " 0.05 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " n_estimators \n",
+ " 300 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " subsample_for_bin \n",
+ " 200000 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " objective \n",
+ " 'multiclass' \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " class_weight \n",
+ " None \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " min_split_gain \n",
+ " 0.0 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " min_child_weight \n",
+ " 0.001 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " min_child_samples \n",
+ " 20 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " subsample \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " subsample_freq \n",
+ " 0 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " colsample_bytree \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " reg_alpha \n",
+ " 0.0 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " reg_lambda \n",
+ " 0.0 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " random_state \n",
+ " 42 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " n_jobs \n",
+ " None \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " importance_type \n",
+ " 'split' \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " num_class \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "LGBMClassifier(learning_rate=0.05, n_estimators=300, num_class=4,\n",
+ " objective='multiclass', random_state=42)"
+ ]
+ },
+ "execution_count": 112,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "lgb.fit(x_train,y_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 113,
+ "id": "3569a45b-ae22-47e0-bef5-508bbb74d69b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tahmin4=lgb.predict(x_val)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 114,
+ "id": "e50403cb-edd2-419a-994b-a2612915ae00",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 0.8853601859024013\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('Accuracy:', accuracy_score(y_val, tahmin4))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 115,
+ "id": "834b3f60-524f-4a8e-b863-d35ce4e2da4a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0.0 0.91 0.91 0.91 395\n",
+ " 1.0 0.84 0.85 0.85 315\n",
+ " 2.0 0.93 0.90 0.92 317\n",
+ " 3.0 0.85 0.87 0.86 264\n",
+ "\n",
+ " accuracy 0.89 1291\n",
+ " macro avg 0.88 0.88 0.88 1291\n",
+ "weighted avg 0.89 0.89 0.89 1291\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(classification_report(y_val, tahmin4))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 116,
+ "id": "51bea5ec-78a3-408d-b3b5-ff6e2c4df00a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['lightgbm_model.pkl']"
+ ]
+ },
+ "execution_count": 116,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "joblib.dump(lgb,'lightgbm_model.pkl')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 117,
+ "id": "cf2ba6ec-b308-4333-b6de-31edc0429c27",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['featurer.pkl']"
+ ]
+ },
+ "execution_count": 117,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "FEATURES=x.columns.tolist()\n",
+ "joblib.dump(FEATURES, 'featurer.pkl')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "33b56338-fee2-4a77-9bbf-22b6a84a1b19",
+ "metadata": {},
+ "source": [
+ "### Submisison"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 105,
+ "id": "3a714a61-8827-4850-990a-a3d6240fc97b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sub=pd.read_csv('sample_submission.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 106,
+ "id": "34d2ee9d-e467-42e1-a9c3-f47bfe46d5a6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
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+ " id system_type\n",
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+ }
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+ "sub.head()"
+ ]
+ },
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+ "execution_count": 102,
+ "id": "02354319-c1e0-4800-9eb8-74f1c44aef43",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000244 seconds.\n",
+ "You can set `force_col_wise=true` to remove the overhead.\n",
+ "[LightGBM] [Info] Total Bins 1296\n",
+ "[LightGBM] [Info] Number of data points in the train set: 6455, number of used features: 10\n",
+ "[LightGBM] [Info] Start training from score -1.184793\n",
+ "[LightGBM] [Info] Start training from score -1.411235\n",
+ "[LightGBM] [Info] Start training from score -1.404271\n",
+ "[LightGBM] [Info] Start training from score -1.585709\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "LGBMClassifier(learning_rate=0.05, n_estimators=300, num_class=4,\n",
+ " objective='multiclass', random_state=42) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. iFitted
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\n",
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+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " boosting_type \n",
+ " 'gbdt' \n",
+ " \n",
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+ " 31 \n",
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+ " -1 \n",
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+ " 0.001 \n",
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+ " \n",
+ " \n",
+ " random_state \n",
+ " 42 \n",
+ " \n",
+ " \n",
+ "\n",
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+ " \n",
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+ " None \n",
+ " \n",
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+ ],
+ "text/plain": [
+ "LGBMClassifier(learning_rate=0.05, n_estimators=300, num_class=4,\n",
+ " objective='multiclass', random_state=42)"
+ ]
+ },
+ "execution_count": 102,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "lgb.fit(x, y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 103,
+ "id": "80d90b56-37ea-4f54-b747-93e66504e6fb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_test=test.drop(columns=['system_type','id'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 104,
+ "id": "85b05bc6-5a0a-4784-8539-a6fa33b9edba",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tahmin5=lgb.predict(x_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 107,
+ "id": "91c14630-ca93-46c7-97dc-fb80814b8c6a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "submission=pd.DataFrame({'id': df2['id'],'system_type': tahmin5})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 110,
+ "id": "74713ccc-2fdb-4ef3-8e27-c0cfda3b475d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "submission.to_csv('submission.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "700039f0-0235-4731-94e0-4bbd8f0cb0d3",
+ "metadata": {},
+ "source": [
+ "### Conclusion\n",
+ "Several machine learning models were tested, and LightGBM achieved the best performance.\n",
+ "The final model reached an accuracy of around 88.5% on the validation set and showed balanced results across all four classes.\n",
+ "This indicates that the model can effectively capture the complex and non-linear relationships in the data.\n",
+ "The trained model and preprocessing steps were saved to allow deployment in a Streamlit application and future reuse."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:base] *",
+ "language": "python",
+ "name": "conda-base-py"
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+ "name": "ipython",
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+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.13.5"
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+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
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