Coding Challenge.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     V1    V2\n",
      "0  4.30  0.80\n",
      "1  3.95  0.80\n",
      "2  3.95  0.75\n",
      "3  3.85  0.85\n",
      "4  4.30  0.80\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as graph\n",
    "import numpy as np\n",
    "from sklearn.mixture import GaussianMixture\n",
    "\n",
    "dataset = pd.read_csv('ClusterPlot.csv')\n",
    "dataset.drop('Unnamed: 0', axis = 1, inplace = True)\n",
    "print(dataset.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x21522022d48>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_values = dataset['V1']\n",
    "y_values = dataset['V2']\n",
    "graph.grid(zorder=0, color=\"lightgrey\")\n",
    "graph.scatter(x_values, y_values, zorder=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x21522179888>]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_array = np.arange(1,15)\n",
    "model_list = []\n",
    "for num in num_array:\n",
    "    model = GaussianMixture(num, covariance_type='full', random_state=0).fit(dataset)\n",
    "    model_list.append(model)\n",
    "\n",
    "aic_list = []\n",
    "bic_list = []\n",
    "for model in model_list:\n",
    "    aic_list.append(model.aic(dataset))\n",
    "    bic_list.append(model.bic(dataset))\n",
    "    \n",
    "graph.plot(num_array, aic_list, color='red')\n",
    "graph.plot(num_array, bic_list, color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2152230d148>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model1 = GaussianMixture(n_components=2).fit(dataset)\n",
    "label1 = model1.predict(dataset)\n",
    "graph.scatter(x_values, y_values, c=label1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x21522377048>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model2 = GaussianMixture(n_components=3).fit(dataset)\n",
    "label2 = model2.predict(dataset)\n",
    "graph.scatter(x_values, y_values, c=label2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9.99999994e-001 6.43147158e-009]\n",
      " [9.99999125e-001 8.75081280e-007]\n",
      " [9.99999454e-001 5.46007648e-007]\n",
      " [9.99993593e-001 6.40725500e-006]\n",
      " [9.99999994e-001 6.43147158e-009]\n",
      " [9.99999995e-001 4.76884157e-009]\n",
      " [9.99999133e-001 8.66971006e-007]\n",
      " [9.99999947e-001 5.27073707e-008]\n",
      " [9.99959296e-001 4.07039956e-005]\n",
      " [9.99999554e-001 4.46310881e-007]\n",
      " [1.00000000e+000 2.71889874e-010]\n",
      " [9.99999489e-001 5.11462041e-007]\n",
      " [9.99998943e-001 1.05728398e-006]\n",
      " [9.99981453e-001 1.85473164e-005]\n",
      " [1.00000000e+000 2.52783687e-013]\n",
      " [1.00000000e+000 3.91099558e-013]\n",
      " [1.00000000e+000 5.54117400e-011]\n",
      " [9.99999988e-001 1.22740318e-008]\n",
      " [1.00000000e+000 2.25156654e-010]\n",
      " [9.99999997e-001 2.95304651e-009]\n",
      " [9.99999982e-001 1.77902762e-008]\n",
      " [9.99999982e-001 1.77902762e-008]\n",
      " [9.99999943e-001 5.71720178e-008]\n",
      " [9.99974293e-001 2.57069190e-005]\n",
      " [9.99978649e-001 2.13505289e-005]\n",
      " [9.99997953e-001 2.04724843e-006]\n",
      " [9.99998797e-001 1.20333662e-006]\n",
      " [9.99999994e-001 5.83903192e-009]\n",
      " [9.99999994e-001 6.43147158e-009]\n",
      " [9.99995962e-001 4.03779485e-006]\n",
      " [9.99995962e-001 4.03779485e-006]\n",
      " [9.99999982e-001 1.77902762e-008]\n",
      " [1.00000000e+000 2.96412844e-011]\n",
      " [1.00000000e+000 1.11123721e-012]\n",
      " [9.99999554e-001 4.46310881e-007]\n",
      " [9.99999946e-001 5.43496483e-008]\n",
      " [1.00000000e+000 2.02900724e-010]\n",
      " [9.99999554e-001 4.46310881e-007]\n",
      " [9.99986486e-001 1.35143952e-005]\n",
      " [9.99999974e-001 2.55562667e-008]\n",
      " [9.99999987e-001 1.33517685e-008]\n",
      " [9.99232203e-001 7.67797396e-004]\n",
      " [9.99996147e-001 3.85272554e-006]\n",
      " [9.99987718e-001 1.22821176e-005]\n",
      " [9.99996449e-001 3.55149565e-006]\n",
      " [9.99996679e-001 3.32087828e-006]\n",
      " [9.99999997e-001 2.95304651e-009]\n",
      " [9.99998300e-001 1.69950684e-006]\n",
      " [9.99999999e-001 5.93723615e-010]\n",
      " [9.99999944e-001 5.59225533e-008]\n",
      " [1.48267805e-082 1.00000000e+000]\n",
      " [3.85122677e-081 1.00000000e+000]\n",
      " [6.34633897e-096 1.00000000e+000]\n",
      " [3.38041204e-062 1.00000000e+000]\n",
      " [2.23963613e-086 1.00000000e+000]\n",
      " [1.04733428e-077 1.00000000e+000]\n",
      " [2.15838033e-093 1.00000000e+000]\n",
      " [3.95968978e-034 1.00000000e+000]\n",
      " [1.21932438e-077 1.00000000e+000]\n",
      " [7.32265536e-062 1.00000000e+000]\n",
      " [2.81547435e-040 1.00000000e+000]\n",
      " [1.91754854e-072 1.00000000e+000]\n",
      " [4.54894363e-051 1.00000000e+000]\n",
      " [1.41581447e-087 1.00000000e+000]\n",
      " [6.24953200e-047 1.00000000e+000]\n",
      " [1.46725623e-072 1.00000000e+000]\n",
      " [4.18659030e-085 1.00000000e+000]\n",
      " [3.55126969e-053 1.00000000e+000]\n",
      " [7.56392846e-086 1.00000000e+000]\n",
      " [2.05896103e-051 1.00000000e+000]\n",
      " [1.76782789e-108 1.00000000e+000]\n",
      " [2.81287478e-058 1.00000000e+000]\n",
      " [6.84872556e-101 1.00000000e+000]\n",
      " [4.90624388e-080 1.00000000e+000]\n",
      " [3.28498903e-067 1.00000000e+000]\n",
      " [2.13427554e-073 1.00000000e+000]\n",
      " [3.18237155e-089 1.00000000e+000]\n",
      " [1.73675886e-110 1.00000000e+000]\n",
      " [5.85533451e-084 1.00000000e+000]\n",
      " [3.73934977e-036 1.00000000e+000]\n",
      " [5.31562884e-049 1.00000000e+000]\n",
      " [4.43815493e-043 1.00000000e+000]\n",
      " [3.55126969e-053 1.00000000e+000]\n",
      " [1.39610296e-114 1.00000000e+000]\n",
      " [7.56392846e-086 1.00000000e+000]\n",
      " [5.73025062e-086 1.00000000e+000]\n",
      " [2.24006289e-088 1.00000000e+000]\n",
      " [1.40796125e-073 1.00000000e+000]\n",
      " [8.16823301e-063 1.00000000e+000]\n",
      " [1.60141456e-061 1.00000000e+000]\n",
      " [1.04739233e-071 1.00000000e+000]\n",
      " [3.56473287e-083 1.00000000e+000]\n",
      " [8.73410422e-057 1.00000000e+000]\n",
      " [3.95968978e-034 1.00000000e+000]\n",
      " [2.08853737e-067 1.00000000e+000]\n",
      " [1.91513145e-062 1.00000000e+000]\n",
      " [2.56496543e-066 1.00000000e+000]\n",
      " [5.31823600e-068 1.00000000e+000]\n",
      " [2.68175541e-028 1.00000000e+000]\n",
      " [3.51717414e-063 1.00000000e+000]\n",
      " [1.47987713e-206 1.00000000e+000]\n",
      " [2.00002850e-129 1.00000000e+000]\n",
      " [5.73338801e-175 1.00000000e+000]\n",
      " [1.49837836e-146 1.00000000e+000]\n",
      " [1.12885716e-177 1.00000000e+000]\n",
      " [3.72259495e-214 1.00000000e+000]\n",
      " [1.87116090e-097 1.00000000e+000]\n",
      " [3.43979056e-180 1.00000000e+000]\n",
      " [5.10671650e-157 1.00000000e+000]\n",
      " [5.67007956e-207 1.00000000e+000]\n",
      " [2.32057464e-129 1.00000000e+000]\n",
      " [7.76393102e-137 1.00000000e+000]\n",
      " [2.03563786e-154 1.00000000e+000]\n",
      " [1.46732954e-130 1.00000000e+000]\n",
      " [3.46989927e-154 1.00000000e+000]\n",
      " [2.66258023e-155 1.00000000e+000]\n",
      " [3.56691988e-140 1.00000000e+000]\n",
      " [3.64988444e-222 1.00000000e+000]\n",
      " [7.31544207e-248 1.00000000e+000]\n",
      " [1.65708007e-107 1.00000000e+000]\n",
      " [5.73338801e-175 1.00000000e+000]\n",
      " [4.96968158e-125 1.00000000e+000]\n",
      " [1.20195977e-214 1.00000000e+000]\n",
      " [2.13763588e-113 1.00000000e+000]\n",
      " [2.64529663e-164 1.00000000e+000]\n",
      " [1.91535077e-162 1.00000000e+000]\n",
      " [7.00557586e-109 1.00000000e+000]\n",
      " [5.41017861e-113 1.00000000e+000]\n",
      " [2.22434974e-162 1.00000000e+000]\n",
      " [3.68957151e-142 1.00000000e+000]\n",
      " [1.67436513e-174 1.00000000e+000]\n",
      " [3.31600737e-190 1.00000000e+000]\n",
      " [7.97012614e-168 1.00000000e+000]\n",
      " [1.76782789e-108 1.00000000e+000]\n",
      " [1.19689572e-128 1.00000000e+000]\n",
      " [2.40245044e-195 1.00000000e+000]\n",
      " [8.66998235e-177 1.00000000e+000]\n",
      " [3.56691988e-140 1.00000000e+000]\n",
      " [7.00557586e-109 1.00000000e+000]\n",
      " [2.98214561e-148 1.00000000e+000]\n",
      " [2.43731779e-176 1.00000000e+000]\n",
      " [4.52496538e-143 1.00000000e+000]\n",
      " [2.00002850e-129 1.00000000e+000]\n",
      " [6.80058462e-187 1.00000000e+000]\n",
      " [6.80058462e-187 1.00000000e+000]\n",
      " [1.36352880e-149 1.00000000e+000]\n",
      " [1.75890303e-123 1.00000000e+000]\n",
      " [3.71697829e-135 1.00000000e+000]\n",
      " [1.17352124e-160 1.00000000e+000]\n",
      " [4.39350159e-123 1.00000000e+000]]\n"
     ]
    }
   ],
   "source": [
    "prob_list_1 = model1.predict_proba(dataset)\n",
    "print(prob_list_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.00000000e+000 2.17161152e-032 3.56861817e-029]\n",
      " [1.00000000e+000 5.59919942e-027 7.36524352e-023]\n",
      " [1.00000000e+000 5.68076781e-028 4.50704518e-024]\n",
      " [1.00000000e+000 1.84795735e-024 5.97220677e-020]\n",
      " [1.00000000e+000 2.17161152e-032 3.56861817e-029]\n",
      " [1.00000000e+000 1.07305056e-031 3.32770785e-028]\n",
      " [1.00000000e+000 1.17881262e-026 1.96211440e-022]\n",
      " [1.00000000e+000 1.05973326e-029 5.98621889e-026]\n",
      " [1.00000000e+000 1.13297065e-022 5.13444821e-018]\n",
      " [1.00000000e+000 1.00145357e-027 1.02065720e-023]\n",
      " [1.00000000e+000 2.32444479e-035 1.15589487e-032]\n",
      " [1.00000000e+000 5.25404452e-027 8.59869980e-023]\n",
      " [1.00000000e+000 3.16673862e-027 3.26218708e-023]\n",
      " [1.00000000e+000 4.69914178e-026 3.68917575e-022]\n",
      " [1.00000000e+000 7.88685279e-045 1.22141487e-044]\n",
      " [1.00000000e+000 1.63300065e-041 4.26688139e-040]\n",
      " [1.00000000e+000 4.70798586e-037 1.03084382e-034]\n",
      " [1.00000000e+000 2.82464885e-031 8.54643213e-028]\n",
      " [1.00000000e+000 7.98336007e-035 6.31350204e-032]\n",
      " [1.00000000e+000 1.70398135e-032 3.43481473e-029]\n",
      " [1.00000000e+000 2.03956839e-030 1.03312556e-026]\n",
      " [1.00000000e+000 2.03956839e-030 1.03312556e-026]\n",
      " [1.00000000e+000 6.88139125e-033 3.40618360e-030]\n",
      " [1.00000000e+000 3.73106291e-023 2.15847318e-018]\n",
      " [1.00000000e+000 4.02816249e-023 2.36150506e-018]\n",
      " [1.00000000e+000 1.62976780e-025 4.27770908e-021]\n",
      " [1.00000000e+000 5.54085995e-026 1.40417597e-021]\n",
      " [1.00000000e+000 4.47786935e-032 9.70969315e-029]\n",
      " [1.00000000e+000 2.17161152e-032 3.56861817e-029]\n",
      " [1.00000000e+000 8.81383113e-025 2.86883126e-020]\n",
      " [1.00000000e+000 8.81383113e-025 2.86883126e-020]\n",
      " [1.00000000e+000 2.03956839e-030 1.03312556e-026]\n",
      " [1.00000000e+000 3.22148703e-038 3.33089575e-036]\n",
      " [1.00000000e+000 9.77147174e-042 1.52869012e-040]\n",
      " [1.00000000e+000 1.00145357e-027 1.02065720e-023]\n",
      " [1.00000000e+000 3.30481704e-031 6.04068661e-028]\n",
      " [1.00000000e+000 9.63744973e-037 1.71674098e-034]\n",
      " [1.00000000e+000 1.00145357e-027 1.02065720e-023]\n",
      " [1.00000000e+000 2.51334986e-024 6.39744955e-020]\n",
      " [1.00000000e+000 1.74718751e-030 7.27389806e-027]\n",
      " [1.00000000e+000 1.36579415e-031 3.15057026e-028]\n",
      " [1.00000000e+000 2.56085086e-019 2.21393935e-014]\n",
      " [1.00000000e+000 9.27830364e-026 1.54508304e-021]\n",
      " [1.00000000e+000 6.68522196e-024 3.14988642e-019]\n",
      " [1.00000000e+000 2.03300003e-025 5.80467584e-021]\n",
      " [1.00000000e+000 3.49511031e-025 9.18181022e-021]\n",
      " [1.00000000e+000 1.70398135e-032 3.43481473e-029]\n",
      " [1.00000000e+000 3.06975032e-026 5.13922153e-022]\n",
      " [1.00000000e+000 1.58593097e-034 1.16381942e-031]\n",
      " [1.00000000e+000 5.09374821e-030 2.22011976e-026]\n",
      " [8.90983826e-083 7.26595114e-004 9.99273405e-001]\n",
      " [2.13072561e-081 3.23101639e-003 9.96768984e-001]\n",
      " [4.84686464e-096 1.24564298e-002 9.87543570e-001]\n",
      " [3.56400601e-062 4.52587464e-002 9.54741254e-001]\n",
      " [1.96018083e-086 2.41949996e-002 9.75805000e-001]\n",
      " [1.42338051e-077 8.63518070e-002 9.13648193e-001]\n",
      " [2.11006941e-093 3.13719892e-002 9.68628011e-001]\n",
      " [2.83398269e-034 3.76535198e-004 9.99623465e-001]\n",
      " [6.44196781e-078 2.35538947e-003 9.97644611e-001]\n",
      " [6.52952982e-062 2.79967213e-002 9.72003279e-001]\n",
      " [1.81743473e-040 4.73368310e-003 9.95266317e-001]\n",
      " [1.13737261e-072 6.41554681e-003 9.93584453e-001]\n",
      " [2.31386901e-051 8.68949304e-004 9.99131051e-001]\n",
      " [2.02851356e-087 9.37459161e-002 9.06254084e-001]\n",
      " [4.55853537e-047 1.63484561e-004 9.99836515e-001]\n",
      " [8.75809853e-073 4.22316129e-004 9.99577684e-001]\n",
      " [8.75809342e-085 2.49686484e-001 7.50313516e-001]\n",
      " [1.86579795e-053 5.25428326e-004 9.99474572e-001]\n",
      " [1.95008963e-085 4.98492708e-001 5.01507292e-001]\n",
      " [1.04107673e-051 1.29096783e-003 9.98709032e-001]\n",
      " [4.18601727e-108 8.72977485e-001 1.27022515e-001]\n",
      " [1.51871838e-058 4.26406963e-004 9.99573593e-001]\n",
      " [1.67634441e-100 8.37539944e-001 1.62460056e-001]\n",
      " [4.53003293e-080 3.00694995e-002 9.69930501e-001]\n",
      " [1.70108402e-067 6.70180348e-004 9.99329820e-001]\n",
      " [1.12431481e-073 8.31914656e-004 9.99168085e-001]\n",
      " [2.41983812e-089 1.43115521e-002 9.85688448e-001]\n",
      " [4.52731062e-110 4.02221444e-001 5.97778556e-001]\n",
      " [7.28947912e-084 6.68142040e-002 9.33185796e-001]\n",
      " [7.13474424e-036 4.06315642e-005 9.99959368e-001]\n",
      " [2.74776208e-049 1.49697647e-003 9.98503024e-001]\n",
      " [2.61697305e-043 4.21931923e-004 9.99578068e-001]\n",
      " [1.86579795e-053 5.25428326e-004 9.99474572e-001]\n",
      " [1.57110008e-114 9.95905798e-001 4.09420153e-003]\n",
      " [1.95008963e-085 4.98492708e-001 5.01507292e-001]\n",
      " [4.39511621e-086 1.55924918e-002 9.84407508e-001]\n",
      " [1.41715806e-088 6.23993704e-003 9.93760063e-001]\n",
      " [1.20011240e-073 2.44578653e-002 9.75542135e-001]\n",
      " [4.16753588e-063 2.58279614e-003 9.97417204e-001]\n",
      " [1.22837930e-061 1.74381708e-002 9.82561829e-001]\n",
      " [1.58337924e-071 1.15383174e-001 8.84616826e-001]\n",
      " [3.19356372e-083 2.68317576e-002 9.73168242e-001]\n",
      " [4.32451891e-057 1.47150174e-003 9.98528498e-001]\n",
      " [2.83398269e-034 3.76535198e-004 9.99623465e-001]\n",
      " [1.67326665e-067 2.03927744e-002 9.79607226e-001]\n",
      " [9.46857308e-063 1.72202938e-003 9.98277971e-001]\n",
      " [1.45544099e-066 5.30070535e-003 9.94699295e-001]\n",
      " [2.63880239e-068 1.40141916e-003 9.98598581e-001]\n",
      " [5.58809797e-028 4.94777210e-005 9.99950522e-001]\n",
      " [1.89609588e-063 3.92652019e-003 9.96073480e-001]\n",
      " [2.20707130e-206 1.00000000e+000 9.37953181e-011]\n",
      " [1.28646374e-129 9.99973477e-001 2.65225995e-005]\n",
      " [3.68080726e-175 9.99992372e-001 7.62826806e-006]\n",
      " [1.01300850e-146 9.99972814e-001 2.71861795e-005]\n",
      " [8.14804707e-178 9.99999922e-001 7.75286816e-008]\n",
      " [2.71503006e-214 9.99999980e-001 2.02535894e-008]\n",
      " [1.16749286e-097 9.99830724e-001 1.69276256e-004]\n",
      " [2.17230329e-180 9.99995803e-001 4.19667311e-006]\n",
      " [3.28158606e-157 9.99998062e-001 1.93839112e-006]\n",
      " [3.42578008e-207 9.99999609e-001 3.91368402e-007]\n",
      " [3.92046279e-129 9.74531877e-001 2.54681233e-002]\n",
      " [5.89774190e-137 9.99834780e-001 1.65220048e-004]\n",
      " [1.55260510e-154 9.99848128e-001 1.51871710e-004]\n",
      " [8.62288523e-131 9.99997351e-001 2.64891022e-006]\n",
      " [2.42117123e-154 9.99999936e-001 6.42157284e-008]\n",
      " [1.84228957e-155 9.99962462e-001 3.75383864e-005]\n",
      " [3.23226503e-140 9.99239762e-001 7.60237979e-004]\n",
      " [1.90742451e-222 9.99998964e-001 1.03582378e-006]\n",
      " [2.54252419e-247 1.00000000e+000 5.65669308e-013]\n",
      " [1.50477320e-107 9.98578459e-001 1.42154056e-003]\n",
      " [3.68080726e-175 9.99992372e-001 7.62826806e-006]\n",
      " [3.25309541e-125 9.99954781e-001 4.52189654e-005]\n",
      " [9.50613441e-215 9.99999991e-001 9.10295909e-009]\n",
      " [3.56962891e-113 9.72880784e-001 2.71192158e-002]\n",
      " [1.84617541e-164 9.99950421e-001 4.95791624e-005]\n",
      " [1.69833355e-162 9.99280618e-001 7.19382291e-004]\n",
      " [1.47001352e-108 9.25365641e-001 7.46343592e-002]\n",
      " [1.03608937e-112 9.51199956e-001 4.88000435e-002]\n",
      " [1.46728271e-162 9.99999509e-001 4.91073412e-007]\n",
      " [5.89450166e-142 9.80611753e-001 1.93882469e-002]\n",
      " [1.09203044e-174 9.99984393e-001 1.56065600e-005]\n",
      " [3.06089717e-190 9.98120534e-001 1.87946633e-003]\n",
      " [5.58568362e-168 9.99999880e-001 1.20123081e-007]\n",
      " [4.18601727e-108 8.72977485e-001 1.27022515e-001]\n",
      " [8.59909434e-129 9.99885126e-001 1.14874144e-004]\n",
      " [1.44027605e-195 9.99997135e-001 2.86516590e-006]\n",
      " [5.78023743e-177 9.99999621e-001 3.78530141e-007]\n",
      " [3.23226503e-140 9.99239762e-001 7.60237979e-004]\n",
      " [1.47001352e-108 9.25365641e-001 7.46343592e-002]\n",
      " [3.07369551e-148 9.98115914e-001 1.88408574e-003]\n",
      " [1.58550868e-176 9.99999181e-001 8.19032063e-007]\n",
      " [5.66992683e-143 9.94114634e-001 5.88536591e-003]\n",
      " [1.28646374e-129 9.99973477e-001 2.65225995e-005]\n",
      " [4.55591819e-187 9.99999763e-001 2.36980343e-007]\n",
      " [4.55591819e-187 9.99999763e-001 2.36980343e-007]\n",
      " [1.09577563e-149 9.99737807e-001 2.62192816e-004]\n",
      " [1.55265770e-123 9.99217337e-001 7.82662851e-004]\n",
      " [3.98618768e-135 9.97547691e-001 2.45230878e-003]\n",
      " [7.70702641e-161 9.99989698e-001 1.03016422e-005]\n",
      " [4.28366990e-123 9.98459395e-001 1.54060513e-003]]\n"
     ]
    }
   ],
   "source": [
    "prob_list_2 = model2.predict_proba(dataset)\n",
    "print(prob_list_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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