Autism Detection.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "import plotly.express as px\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score\n",
    "from plotly.offline import init_notebook_mode\n",
    "warnings.filterwarnings(\"ignore\")\n",
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   "cell_type": "code",
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   "id": "637868f4",
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   "source": [
    "df = pd.read_csv(\"autism_screening.csv\")"
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   "id": "cd9e8a22",
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   "source": [
    "df.head()"
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    "df.shape"
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    "df.info()"
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   "id": "7d47e1e6",
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     "data": {
      "text/plain": [
       "White-European     233\n",
       "Asian              123\n",
       "?                   95\n",
       "Middle Eastern      92\n",
       "Black               43\n",
       "South Asian         36\n",
       "Others              30\n",
       "Latino              20\n",
       "Hispanic            13\n",
       "Pasifika            12\n",
       "Turkish              6\n",
       "others               1\n",
       "Name: ethnicity, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "df[\"ethnicity\"].value_counts()"
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   "id": "fa6547a0",
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   "source": [
    "df[\"ethnicity\"] = df[\"ethnicity\"].str.replace(\"others\", \"Others\")\n",
    "df[\"ethnicity\"] = df[\"ethnicity\"].str.replace(\"?\", \"Others\")"
   ]
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   "cell_type": "code",
   "execution_count": 8,
   "id": "003b0773",
   "metadata": {},
   "outputs": [],
   "source": [
    "#jundice - austim - used_app_before - Class/ASD\n",
    "df[\"jundice\"] = df[\"jundice\"].map({\"no\": 0, \"yes\": 1})\n",
    "df[\"austim\"] = df[\"austim\"].map({\"no\": 0, \"yes\": 1})\n",
    "df[\"used_app_before\"] = df[\"used_app_before\"].map({\"no\": 0, \"yes\": 1})"
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   "id": "ffe0e129",
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   "source": [
    "df[\"Class/ASD\"] = df[\"Class/ASD\"].map({\"NO\": 0, \"YES\": 1})"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "classes = df[\"Class/ASD\"].value_counts()\n",
    "\n",
    "values = classes.values\n",
    "names = classes.index\n",
    "\n",
    "fig = px.pie(df, values=values, names=names, hole=0.5)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8ab3a09a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 5))\n",
    "sns.countplot(x=df[\"ethnicity\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b95cafc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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k/vz5Wb16dY81bW1tGTNmTG0NAAAAALBxqOuZke9nyZIlSZLm5uYe25ubm/Ob3/ymtmbAgAHZaqut1lnz9uvfzapVq7Jq1ara866urt4aGwAAAAB4DxvtmZFvq1QqPZ5Xq9V1tr3TB62ZOXNmmpqaao/29vZemRUAAAAAeG8bbYxsaWlJknXOcFy6dGntbMmWlpZ0d3dn2bJl77nm3Zx77rnp7OysPV5++eVenh4AAAAAeKeNNkaOGjUqLS0tmTt3bm1bd3d35s2bl/HjxydJxo0bl/79+/dY09HRkWeeeaa25t00NjZmyJAhPR4AAAAAwIZV12tGvv7663nxxRdrzxcuXJinnnoqw4YNyyc+8YlMmTIlF110UUaPHp3Ro0fnoosuyhZbbJEvf/nLSZKmpqacfPLJmTZtWoYPH55hw4blrLPOytixY2t31wYAAAAANg51jZG/+MUvsv/++9eeT506NUly4oknZs6cOZk+fXreeOONnHbaaVm2bFn23HPP3HPPPRk8eHDtNZdddln69euXY489Nm+88UYOPPDAzJkzJw0NDcWPBwAAAAB4b5VqtVqt9xD11tXVlaampnR2dm5yP9ked/Z19R4B2EBuHfz9eo8AbCCf+PaCeo9AQf5eg75r/vdPqPcIAL3mw/a1jfaakQAAAABA3yJGAgAAAABFiJEAAAAAQBFiJAAAAABQhBgJAAAAABQhRgIAAAAARYiRAAAAAEARYiQAAAAAUIQYCQAAAAAUIUYCAAAAAEWIkQAAAABAEWIkAAAAAFCEGAkAAAAAFCFGAgAAAABFiJEAAAAAQBFiJAAAAABQhBgJAAAAABQhRgIAAAAARYiRAAAAAEARYiQAAAAAUIQYCQAAAAAUIUYCAAAAAEWIkQAAAABAEWIkAAAAAFCEGAkAAAAAFCFGAgAAAABFiJEAAAAAQBFiJAAAAABQhBgJAAAAABQhRgIAAAAARYiRAAAAAEARYiQAAAAAUIQYCQAAAAAUIUYCAAAAAEWIkQAAAABAEWIkAAAAAFCEGAkAAAAAFNGv3gMAAADApmjRBWPrPQKwgXzi2wvqPcJGy5mRAAAAAEARYiQAAAAAUIQYCQAAAAAUIUYCAAAAAEWIkQAAAABAEWIkAAAAAFCEGAkAAAAAFCFGAgAAAABFiJEAAAAAQBFiJAAAAABQhBgJAAAAABQhRgIAAAAARYiRAAAAAEARYiQAAAAAUIQYCQAAAAAUIUYCAAAAAEWIkQAAAABAEWIkAAAAAFCEGAkAAAAAFCFGAgAAAABFiJEAAAAAQBFiJAAAAABQhBgJAAAAABQhRgIAAAAARYiRAAAAAEARYiQAAAAAUIQYCQAAAAAUIUYCAAAAAEWIkQAAAABAEWIkAAAAAFCEGAkAAAAAFCFGAgAAAABFiJEAAAAAQBFiJAAAAABQhBgJAAAAABQhRgIAAAAARYiRAAAAAEARYiQAAAAAUIQYCQAAAAAUIUYCAAAAAEWIkQAAAABAEWIkAAAAAFCEGAkAAAAAFCFGAgAAAABFiJEAAAAAQBFiJAAAAABQhBgJAAAAABQhRgIAAAAARYiRAAAAAEARYiQAAAAAUIQYCQAAAAAUIUYCAAAAAEWIkQAAAABAEWIkAAAAAFCEGAkAAAAAFCFGAgAAAABFiJEAAAAAQBFiJAAAAABQhBgJAAAAABQhRgIAAAAARYiRAAAAAEARYiQAAAAAUIQYCQAAAAAUIUYCAAAAAEWIkQAAAABAEWIkAAAAAFCEGAkAAAAAFCFGAgAAAABFiJEAAAAAQBFiJAAAAABQhBgJAAAAABQhRgIAAAAARYiRAAAAAEARYiQAAAAAUIQYCQAAAAAUIUYCAAAAAEWIkQAAAABAEWIkAAAAAFCEGAkAAAAAFCFGAgAAAABFiJEAAAAAQBFiJAAAAABQhBgJAAAAABQhRgIAAAAARYiRAAAAAEARYiQAAAAAUIQYCQAAAAAUIUYCAAAAAEWIkQAAAABAEWIkAAAAAFCEGAkAAAAAFCFGAgAAAABFiJEAAAAAQBFiJAAAAABQhBgJAAAAABQhRgIAAAAARYiRAAAAAEARYiQAAAAAUIQYCQAAAAAUIUYCAAAAAEWIkQAAAABAEWIkAAAAAFCEGAkAAAAAFCFGAgAAAABFiJEAAAAAQBFiJAAAAABQhBgJAAAAABQhRgIAAAAARWzUMXLGjBmpVCo9Hi0tLbX91Wo1M2bMSFtbWwYOHJgJEybk2WefrePEAAAAAMB72ahjZJLsvPPO6ejoqD0WLFhQ23fxxRfn0ksvzZVXXpnHH388LS0t+dznPpfXXnutjhMDAAAAAO9mo4+R/fr1S0tLS+2x9dZbJ/nDWZGXX355zjvvvBx11FEZM2ZMrr322qxcuTI33nhjnacGAAAAAN5po4+RL7zwQtra2jJq1Kgcd9xx+fWvf50kWbhwYZYsWZKJEyfW1jY2Nma//fbLo48++r7vuWrVqnR1dfV4AAAAAAAb1kYdI/fcc89cd911ufvuu/PDH/4wS5Ysyfjx4/Pqq69myZIlSZLm5uYer2lubq7tey8zZ85MU1NT7dHe3r7BjgEAAAAA+IONOkZOmjQpRx99dMaOHZuDDjood9xxR5Lk2muvra2pVCo9XlOtVtfZ9k7nnntuOjs7a4+XX36594cHAAAAAHrYqGPkOw0aNChjx47NCy+8ULur9jvPgly6dOk6Z0u+U2NjY4YMGdLjAQAAAABsWB+rGLlq1ar88pe/TGtra0aNGpWWlpbMnTu3tr+7uzvz5s3L+PHj6zglAAAAAPBu+tV7gPdz1lln5bDDDssnPvGJLF26NN/73vfS1dWVE088MZVKJVOmTMlFF12U0aNHZ/To0bnooouyxRZb5Mtf/nK9RwcAAAAA3mGjjpGLFy/OX/7lX+aVV17J1ltvnb322iuPPfZYtttuuyTJ9OnT88Ybb+S0007LsmXLsueee+aee+7J4MGD6zw5AAAAAPBOG3WMvOmmm953f6VSyYwZMzJjxowyAwEAAAAA6+1jdc1IAAAAAODjS4wEAAAAAIoQIwEAAACAIsRIAAAAAKAIMRIAAAAAKEKMBAAAAACKECMBAAAAgCLESAAAAACgCDESAAAAAChCjAQAAAAAihAjAQAAAIAixEgAAAAAoAgxEgAAAAAoQowEAAAAAIoQIwEAAACAIsRIAAAAAKAIMRIAAAAAKEKMBAAAAACKECMBAAAAgCLESAAAAACgCDESAAAAAChCjAQAAAAAihAjAQAAAIAixEgAAAAAoAgxEgAAAAAoQowEAAAAAIoQIwEAAACAIsRIAAAAAKAIMRIAAAAAKEKMBAAAAACKECMBAAAAgCLESAAAAACgCDESAAAAAChCjAQAAAAAihAjAQAAAIAixEgAAAAAoAgxEgAAAAAoQowEAAAAAIoQIwEAAACAIsRIAAAAAKAIMRIAAAAAKEKMBAAAAACKECMBAAAAgCLESAAAAACgCDESAAAAAChCjAQAAAAAihAjAQAAAIAixEgAAAAAoAgxEgAAAAAoQowEAAAAAIoQIwEAAACAIsRIAAAAAKAIMRIAAAAAKEKMBAAAAACKECMBAAAAgCLESAAAAACgCDESAAAAAChCjAQAAAAAihAjAQAAAIAixEgAAAAAoAgxEgAAAAAoQowEAAAAAIoQIwEAAACAIsRIAAAAAKAIMRIAAAAAKEKMBAAAAACKECMBAAAAgCLESAAAAACgCDESAAAAAChCjAQAAAAAihAjAQAAAIAixEgAAAAAoAgxEgAAAAAoQowEAAAAAIoQIwEAAACAIsRIAAAAAKAIMRIAAAAAKEKMBAAAAACKECMBAAAAgCLESAAAAACgCDESAAAAAChCjAQAAAAAihAjAQAAAIAixEgAAAAAoAgxEgAAAAAoQowEAAAAAIoQIwEAAACAIsRIAAAAAKAIMRIAAAAAKEKMBAAAAACKECMBAAAAgCLESAAAAACgCDESAAAAAChCjAQAAAAAihAjAQAAAIAixEgAAAAAoAgxEgAAAAAoQowEAAAAAIoQIwEAAACAIsRIAAAAAKAIMRIAAAAAKEKMBAAAAACKECMBAAAAgCLESAAAAACgCDESAAAAAChCjAQAAAAAihAjAQAAAIAixEgAAAAAoAgxEgAAAAAoQowEAAAAAIoQIwEAAACAIsRIAAAAAKAIMRIAAAAAKEKMBAAAAACKECMBAAAAgCLESAAAAACgCDESAAAAAChCjAQAAAAAihAjAQAAAIAixEgAAAAAoAgxEgAAAAAoQowEAAAAAIoQIwEAAACAIsRIAAAAAKAIMRIAAAAAKEKMBAAAAACKECMBAAAAgCLESAAAAACgCDESAAAAAChCjAQAAAAAihAjAQAAAIAixEgAAAAAoAgxEgAAAAAoQowEAAAAAIoQIwEAAACAIsRIAAAAAKAIMRIAAAAAKEKMBAAAAACKECMBAAAAgCLESAAAAACgCDESAAAAAChCjAQAAAAAihAjAQAAAIAixEgAAAAAoAgxEgAAAAAoQowEAAAAAIroMzHyBz/4QUaNGpXNN98848aNy0MPPVTvkQAAAACA/6BPxMibb745U6ZMyXnnnZcnn3wy++yzTyZNmpRFixbVezQAAAAA4P/pEzHy0ksvzcknn5xTTjklO+20Uy6//PK0t7dn1qxZ9R4NAAAAAPh/+tV7gD9Wd3d35s+fn//yX/5Lj+0TJ07Mo48++q6vWbVqVVatWlV73tnZmSTp6uracINupNaseqPeIwAbyGv919R7BGAD2RT/ZtmU+XsN+i5/r0HftSn+vfb2MVer1fdd97GPka+88krWrFmT5ubmHtubm5uzZMmSd33NzJkz893vfned7e3t7RtkRoB6GFPvAYANZ2ZTvScAoBf4ew36sE3477XXXnstTU3vffwf+xj5tkql0uN5tVpdZ9vbzj333EydOrX2fO3atfn973+f4cOHv+drAD5Ourq60t7enpdffjlDhgyp9zgAALyDv9eAvqZarea1115LW1vb+6772MfIESNGpKGhYZ2zIJcuXbrO2ZJva2xsTGNjY49tQ4cO3VAjAtTNkCFD/HELALAR8/ca0Je83xmRb/vY38BmwIABGTduXObOndtj+9y5czN+/Pg6TQUAAAAAvNPH/szIJJk6dWqOP/747Lbbbtl7771z1VVXZdGiRTn11FPrPRoAAAAA8P/0iRj5pS99Ka+++mouuOCCdHR0ZMyYMbnzzjuz3Xbb1Xs0gLpobGzMd77znXUuSQEAwMbB32vApqpS/aD7bQMAAAAA9IKP/TUjAQAAAICPBzESAAAAAChCjAQAAAAAihAjAfqgarWa//yf/3OGDRuWSqWSp556qt4jAQAAgBvYAPRFP/vZz3LEEUfkgQceyPbbb58RI0akX79+9R4LAACATZz/MgXog371q1+ltbU148ePr/coAAAAUONn2gB9zEknnZTJkydn0aJFqVQq+eQnP1nvkQAANmkTJkzI5MmTM2XKlGy11VZpbm7OVVddlRUrVuRrX/taBg8enB122CE/+9nP6j0qwAYnRgL0Mf/4j/+YCy64INtuu206Ojry+OOP13skAIBN3rXXXpsRI0bk5z//eSZPnpxvfvObOeaYYzJ+/Pg88cQTOfjgg3P88cdn5cqV9R4VYINyzUiAPujyyy/P5ZdfnpdeeqneowAAbPImTJiQNWvW5KGHHkqSrFmzJk1NTTnqqKNy3XXXJUmWLFmS1tbW/Ou//mv22muveo4LsEE5MxIAAAA2sM985jO1fzc0NGT48OEZO3ZsbVtzc3OSZOnSpcVnAyhJjAQAAIANrH///j2eVyqVHtsqlUqSZO3atUXnAihNjAQAAAAAihAjAQAAAIAixEgAAAAAoAh30wYAAAAAinBmJAAAAABQhBgJAAAAABQhRgIAAAAARYiRAAAAAEARYiQAAAAAUIQYCQAAAAAUIUYCAAAAAEWIkQAAfOyddNJJOfLII+s9BgAAH0CMBAAAAACKECMBANjkVavVvPXWW/UeAwCgzxMjAQDoNa+99lq+8pWvZNCgQWltbc1ll12WCRMmZMqUKUmS7u7uTJ8+Pdtss00GDRqUPffcMw888EDt9XPmzMnQoUNz9913Z6eddsqWW26ZQw45JB0dHbU1a9asydSpUzN06NAMHz4806dPT7Va7TFHtVrNxRdfnO233z4DBw7MLrvskh//+Me1/Q888EAqlUruvvvu7LbbbmlsbMxDDz20Qb8bAADESAAAetHUqVPzyCOP5Cc/+Unmzp2bhx56KE888URt/9e+9rU88sgjuemmm/L000/nmGOOySGHHJIXXnihtmblypX5h3/4h1x//fV58MEHs2jRopx11lm1/ZdcckmuueaaXH311Xn44Yfz+9//PrfeemuPOf72b/82s2fPzqxZs/Lss8/mzDPPzFe/+tXMmzevx7rp06dn5syZ+eUvf5nPfOYzG+hbAQDgbZXqO/83MgAArIfXXnstw4cPz4033pgvfvGLSZLOzs60tbXlG9/4RiZPnpzRo0dn8eLFaWtrq73uoIMOyh577JGLLrooc+bMyde+9rW8+OKL2WGHHZIkP/jBD3LBBRdkyZIlSZK2trb8zd/8Tc4555wkyVtvvZVRo0Zl3Lhxue2227JixYqMGDEi9913X/bee+/a55xyyilZuXJlbrzxxjzwwAPZf//9c9ttt+WII44o9RUBAGzy+tV7AAAA+oZf//rXWb16dfbYY4/atqampvzJn/xJkuSJJ55ItVrNjjvu2ON1q1atyvDhw2vPt9hii1qITJLW1tYsXbo0yR/iZkdHR4/I2K9fv+y22261n2o/99xzefPNN/O5z32ux+d0d3dn11137bFtt912+2MOGQCAj0iMBACgV7wdAyuVyrtuX7t2bRoaGjJ//vw0NDT0WLPlllvW/t2/f/8e+yqVyjrXhHw/a9euTZLccccd2WabbXrsa2xs7PF80KBBH/p9AQD444mRAAD0ih122CH9+/fPz3/+87S3tydJurq68sILL2S//fbLrrvumjVr1mTp0qXZZ5991uszmpqa0tramsceeyz77rtvkj/8THv+/Pn58z//8yTJpz/96TQ2NmbRokXZb7/9eufgAADoFWIkAAC9YvDgwTnxxBNz9tlnZ9iwYRk5cmS+853vZLPNNkulUsmOO+6Yr3zlKznhhBNyySWXZNddd80rr7yS++67L2PHjs1f/MVffKjP+Zu/+Zv8/d//fUaPHp2ddtopl156aZYvX95jjrPOOitnnnlm1q5dm89+9rPp6urKo48+mi233DInnnjiBvoGAAD4IGIkAAC95tJLL82pp56aQw89NEOGDMn06dPz8ssvZ/PNN0+SzJ49O9/73vcybdq0/J//838yfPjw7L333h86RCbJtGnT0tHRkZNOOimbbbZZvv71r+cLX/hCOjs7a2v+7u/+LiNHjszMmTPz61//OkOHDs2f//mf51vf+lavHzMAAB+eu2kDALDBrFixIttss00uueSSnHzyyfUeBwCAOnNmJAAAvebJJ5/Mv/3bv2WPPfZIZ2dnLrjggiTJEUccUefJAADYGIiRAAD0qn/4h3/I888/nwEDBmTcuHF56KGHMmLEiHqPBQDARsDPtAEAAACAIjar9wAAAAAAwKZBjAQAAAAAihAjAQAAAIAixEgAAAAAoAgxEgAAAAAoQowEAAAAAIoQIwEAAACAIsRIAAAAAKAIMRIAAAAAKOL/A39ixolknZ7MAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1600x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 12))\n",
    "sns.countplot(x=df[\"gender\"], hue=df[\"Class/ASD\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab02fad7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "399bb440",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna(how=\"any\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50b8d722",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3085556c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Self                        522\n",
       "?                            93\n",
       "Parent                       50\n",
       "Relative                     28\n",
       "Others                        5\n",
       "Health care professional      4\n",
       "Name: relation, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"relation\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2294ee45",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"relation\"] = df[\"relation\"].str.replace(\"?\", \"Others\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "537a3ac4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "02e41f04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "White-European     233\n",
       "Others             124\n",
       "Asian              123\n",
       "Middle Eastern      92\n",
       "Black               43\n",
       "South Asian         36\n",
       "Latino              20\n",
       "Hispanic            13\n",
       "Pasifika            12\n",
       "Turkish              6\n",
       "Name: ethnicity, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"ethnicity\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ed2ed31a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def label_encoder(column):\n",
    "    le = LabelEncoder().fit(column)\n",
    "    print(column.name, le.classes_)\n",
    "    return le.transform(column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "eb63c76e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ethnicity ['Asian' 'Black' 'Hispanic' 'Latino' 'Middle Eastern ' 'Others' 'Pasifika'\n",
      " 'South Asian' 'Turkish' 'White-European']\n"
     ]
    }
   ],
   "source": [
    "df[\"ethnicity\"] = label_encoder(df[\"ethnicity\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7e6c11ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "relation ['Health care professional' 'Others' 'Parent' 'Relative' 'Self']\n"
     ]
    }
   ],
   "source": [
    "df[\"relation\"] = label_encoder(df[\"relation\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b4b25960",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender ['f' 'm']\n"
     ]
    }
   ],
   "source": [
    "df[\"gender\"] = label_encoder(df[\"gender\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fb61b11",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "031133fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "ndf = df.drop([\"contry_of_res\", \"age_desc\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "90c31126",
   "metadata": {},
   "outputs": [],
   "source": [
    "ndf[\"age\"] = ndf[\"age\"].astype(\"int64\")\n",
    "ndf[\"result\"] = ndf[\"result\"].astype(\"int64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0159ab5",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "sns.heatmap(ndf.corr() > 0.8, annot=True, cbar=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ed6e686f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = ndf.drop([\"Class/ASD\"], axis=1)\n",
    "y = ndf[\"Class/ASD\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9f908347",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=4242)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a8622294",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: /~https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1c8a7bd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = logreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7c9bb049",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "dc4bf252",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: /~https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_model = XGBClassifier()\n",
    "xgb_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1b8d73d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = xgb_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c999ada3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b094b747",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: /~https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_model = SVC(kernel=\"rbf\")\n",
    "svc_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "37c84a95",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = svc_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a6c077cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9432624113475178"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16d62d11",
   "metadata": {},
   "outputs": [],
   "source": [
    "ndf.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1881f40",
   "metadata": {},
   "outputs": [],
   "source": [
    "ndf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "55748d42",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = np.array([[1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 32, 1, 9, 0, 0, 0, 6, 4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "077c7c74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0]), array([0]), array([0]))"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.predict(test), xgb_model.predict(test), svc_model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f9b450b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(logreg, open(\"logreg.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "633f4b24",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(svc_model, open(\"svc.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e2dd0187",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(xgb_model, open(\"xgb.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8bc91c15",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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