thyroid_csv.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UlPfDU2UlEXm"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "I0Jd0rFklWTt"
   },
   "outputs": [],
   "source": [
    "data=pd.read_csv('hypothyroid.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 232
    },
    "colab_type": "code",
    "id": "CvafJaY_le8l",
    "outputId": "4dab27d2-fb02-46d9-910f-442b6ccd0974"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>on_thyroxine</th>\n",
       "      <th>query_on_thyroxine</th>\n",
       "      <th>on_antithyroid_medication</th>\n",
       "      <th>thyroid_surgery</th>\n",
       "      <th>query_hypothyroid</th>\n",
       "      <th>query_hyperthyroid</th>\n",
       "      <th>pregnant</th>\n",
       "      <th>...</th>\n",
       "      <th>T3_measured</th>\n",
       "      <th>T3</th>\n",
       "      <th>TT4_measured</th>\n",
       "      <th>TT4</th>\n",
       "      <th>T4U_measured</th>\n",
       "      <th>T4U</th>\n",
       "      <th>FTI_measured</th>\n",
       "      <th>FTI</th>\n",
       "      <th>TBG_measured</th>\n",
       "      <th>TBG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>hypothyroid</td>\n",
       "      <td>72</td>\n",
       "      <td>M</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>...</td>\n",
       "      <td>y</td>\n",
       "      <td>0.6</td>\n",
       "      <td>y</td>\n",
       "      <td>15</td>\n",
       "      <td>y</td>\n",
       "      <td>1.48</td>\n",
       "      <td>y</td>\n",
       "      <td>10</td>\n",
       "      <td>n</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>hypothyroid</td>\n",
       "      <td>15</td>\n",
       "      <td>F</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>...</td>\n",
       "      <td>y</td>\n",
       "      <td>1.7</td>\n",
       "      <td>y</td>\n",
       "      <td>19</td>\n",
       "      <td>y</td>\n",
       "      <td>1.13</td>\n",
       "      <td>y</td>\n",
       "      <td>17</td>\n",
       "      <td>n</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>hypothyroid</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>...</td>\n",
       "      <td>y</td>\n",
       "      <td>0.2</td>\n",
       "      <td>y</td>\n",
       "      <td>4</td>\n",
       "      <td>y</td>\n",
       "      <td>1</td>\n",
       "      <td>y</td>\n",
       "      <td>0</td>\n",
       "      <td>n</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>hypothyroid</td>\n",
       "      <td>24</td>\n",
       "      <td>F</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>...</td>\n",
       "      <td>y</td>\n",
       "      <td>0.4</td>\n",
       "      <td>y</td>\n",
       "      <td>6</td>\n",
       "      <td>y</td>\n",
       "      <td>1.04</td>\n",
       "      <td>y</td>\n",
       "      <td>6</td>\n",
       "      <td>n</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>hypothyroid</td>\n",
       "      <td>77</td>\n",
       "      <td>M</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>...</td>\n",
       "      <td>y</td>\n",
       "      <td>1.2</td>\n",
       "      <td>y</td>\n",
       "      <td>57</td>\n",
       "      <td>y</td>\n",
       "      <td>1.28</td>\n",
       "      <td>y</td>\n",
       "      <td>44</td>\n",
       "      <td>n</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0 Age Sex on_thyroxine query_on_thyroxine  \\\n",
       "0  hypothyroid  72   M            f                  f   \n",
       "1  hypothyroid  15   F            t                  f   \n",
       "2  hypothyroid  24   M            f                  f   \n",
       "3  hypothyroid  24   F            f                  f   \n",
       "4  hypothyroid  77   M            f                  f   \n",
       "\n",
       "  on_antithyroid_medication thyroid_surgery query_hypothyroid  \\\n",
       "0                         f               f                 f   \n",
       "1                         f               f                 f   \n",
       "2                         f               f                 f   \n",
       "3                         f               f                 f   \n",
       "4                         f               f                 f   \n",
       "\n",
       "  query_hyperthyroid pregnant  ... T3_measured   T3 TT4_measured TT4  \\\n",
       "0                  f        f  ...           y  0.6            y  15   \n",
       "1                  f        f  ...           y  1.7            y  19   \n",
       "2                  f        f  ...           y  0.2            y   4   \n",
       "3                  f        f  ...           y  0.4            y   6   \n",
       "4                  f        f  ...           y  1.2            y  57   \n",
       "\n",
       "  T4U_measured   T4U FTI_measured FTI TBG_measured TBG  \n",
       "0            y  1.48            y  10            n   ?  \n",
       "1            y  1.13            y  17            n   ?  \n",
       "2            y     1            y   0            n   ?  \n",
       "3            y  1.04            y   6            n   ?  \n",
       "4            y  1.28            y  44            n   ?  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "colab_type": "code",
    "id": "L5Uvlg_Iliq-",
    "outputId": "49b14cc8-676c-4b82-d81d-289ae73df579"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x139ca5590>"
      ]
     },
     "execution_count": 28,
     "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": [
    "sns.countplot(data['Unnamed: 0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 470
    },
    "colab_type": "code",
    "id": "UMylXJwIoxb7",
    "outputId": "87c7cc8f-98d9-47fa-8db3-68b772b03085"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnamed: 0                   0\n",
      "Age                          0\n",
      "Sex                          0\n",
      "on_thyroxine                 0\n",
      "query_on_thyroxine           0\n",
      "on_antithyroid_medication    0\n",
      "thyroid_surgery              0\n",
      "query_hypothyroid            0\n",
      "query_hyperthyroid           0\n",
      "pregnant                     0\n",
      "sick                         0\n",
      "tumor                        0\n",
      "lithium                      0\n",
      "goitre                       0\n",
      "TSH_measured                 0\n",
      "TSH                          0\n",
      "T3_measured                  0\n",
      "T3                           0\n",
      "TT4_measured                 0\n",
      "TT4                          0\n",
      "T4U_measured                 0\n",
      "T4U                          0\n",
      "FTI_measured                 0\n",
      "FTI                          0\n",
      "TBG_measured                 0\n",
      "TBG                          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "data_copy = data.copy(deep = True)\n",
    "data_copy.replace(to_replace='?', inplace=True, value=0)\n",
    "print(data_copy.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "itPF0EZbpCJL"
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'M'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_label.py\u001b[0m in \u001b[0;36m_encode\u001b[0;34m(values, uniques, encode, check_unknown)\u001b[0m\n\u001b[1;32m    111\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 112\u001b[0;31m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_encode_python\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muniques\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    113\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_label.py\u001b[0m in \u001b[0;36m_encode_python\u001b[0;34m(values, uniques, encode)\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0muniques\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m         \u001b[0muniques\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     61\u001b[0m         \u001b[0muniques\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: '<' not supported between instances of 'str' and 'int'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-60-160b59b4187b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m           \u001b[0mdata_copy\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_copy\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m         \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_label.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, y)\u001b[0m\n\u001b[1;32m    251\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolumn_or_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwarn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 252\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    253\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_label.py\u001b[0m in \u001b[0;36m_encode\u001b[0;34m(values, uniques, encode, check_unknown)\u001b[0m\n\u001b[1;32m    113\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"argument must be a string or number\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    115\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: argument must be a string or number",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-60-160b59b4187b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m           \u001b[0mdata_copy\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_copy\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m           \u001b[0mdata_copy\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata_copy\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-60-160b59b4187b>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      8\u001b[0m           \u001b[0mdata_copy\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_copy\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m           \u001b[0mdata_copy\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata_copy\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'M'"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder \n",
    "#LabelEncoder is a utility class to help normalize labels such that they contain only values between 0 and n_classes-1\n",
    "encoder = LabelEncoder()\n",
    "columns=data_copy.columns\n",
    "for cols in columns:\n",
    "    #print(cols)\n",
    "    if(isinstance(data_copy[cols].values[0],str)):\n",
    "        try:\n",
    "          data_copy[cols]=encoder.fit_transform(data_copy[cols].values)\n",
    "        except:\n",
    "          data_copy[cols]=np.array([float(v) for v in data_copy[cols].values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 232
    },
    "colab_type": "code",
    "id": "aCMTtaNsqoKA",
    "outputId": "d841ea5f-52de-4294-d319-a8c0883fd493"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>on_thyroxine</th>\n",
       "      <th>query_on_thyroxine</th>\n",
       "      <th>on_antithyroid_medication</th>\n",
       "      <th>thyroid_surgery</th>\n",
       "      <th>query_hypothyroid</th>\n",
       "      <th>query_hyperthyroid</th>\n",
       "      <th>pregnant</th>\n",
       "      <th>...</th>\n",
       "      <th>T3_measured</th>\n",
       "      <th>T3</th>\n",
       "      <th>TT4_measured</th>\n",
       "      <th>TT4</th>\n",
       "      <th>T4U_measured</th>\n",
       "      <th>T4U</th>\n",
       "      <th>FTI_measured</th>\n",
       "      <th>FTI</th>\n",
       "      <th>TBG_measured</th>\n",
       "      <th>TBG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>M</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>...</td>\n",
       "      <td>y</td>\n",
       "      <td>0.6</td>\n",
       "      <td>y</td>\n",
       "      <td>15</td>\n",
       "      <td>y</td>\n",
       "      <td>1.48</td>\n",
       "      <td>y</td>\n",
       "      <td>10</td>\n",
       "      <td>n</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>F</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>...</td>\n",
       "      <td>y</td>\n",
       "      <td>1.7</td>\n",
       "      <td>y</td>\n",
       "      <td>19</td>\n",
       "      <td>y</td>\n",
       "      <td>1.13</td>\n",
       "      <td>y</td>\n",
       "      <td>17</td>\n",
       "      <td>n</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>M</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>...</td>\n",
       "      <td>y</td>\n",
       "      <td>0.2</td>\n",
       "      <td>y</td>\n",
       "      <td>4</td>\n",
       "      <td>y</td>\n",
       "      <td>1</td>\n",
       "      <td>y</td>\n",
       "      <td>0</td>\n",
       "      <td>n</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>F</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>...</td>\n",
       "      <td>y</td>\n",
       "      <td>0.4</td>\n",
       "      <td>y</td>\n",
       "      <td>6</td>\n",
       "      <td>y</td>\n",
       "      <td>1.04</td>\n",
       "      <td>y</td>\n",
       "      <td>6</td>\n",
       "      <td>n</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>M</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>...</td>\n",
       "      <td>y</td>\n",
       "      <td>1.2</td>\n",
       "      <td>y</td>\n",
       "      <td>57</td>\n",
       "      <td>y</td>\n",
       "      <td>1.28</td>\n",
       "      <td>y</td>\n",
       "      <td>44</td>\n",
       "      <td>n</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0   Age Sex on_thyroxine query_on_thyroxine  \\\n",
       "0           0  72.0   M            f                  f   \n",
       "1           0  15.0   F            t                  f   \n",
       "2           0  24.0   M            f                  f   \n",
       "3           0  24.0   F            f                  f   \n",
       "4           0  77.0   M            f                  f   \n",
       "\n",
       "  on_antithyroid_medication thyroid_surgery query_hypothyroid  \\\n",
       "0                         f               f                 f   \n",
       "1                         f               f                 f   \n",
       "2                         f               f                 f   \n",
       "3                         f               f                 f   \n",
       "4                         f               f                 f   \n",
       "\n",
       "  query_hyperthyroid pregnant  ... T3_measured   T3 TT4_measured TT4  \\\n",
       "0                  f        f  ...           y  0.6            y  15   \n",
       "1                  f        f  ...           y  1.7            y  19   \n",
       "2                  f        f  ...           y  0.2            y   4   \n",
       "3                  f        f  ...           y  0.4            y   6   \n",
       "4                  f        f  ...           y  1.2            y  57   \n",
       "\n",
       "  T4U_measured   T4U FTI_measured FTI TBG_measured TBG  \n",
       "0            y  1.48            y  10            n   0  \n",
       "1            y  1.13            y  17            n   0  \n",
       "2            y     1            y   0            n   0  \n",
       "3            y  1.04            y   6            n   0  \n",
       "4            y  1.28            y  44            n   0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Rnj9rycTls2d"
   },
   "outputs": [],
   "source": [
    "x=data_copy.drop('Unnamed: 0',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "L2CC83OsoFPI"
   },
   "outputs": [],
   "source": [
    "y=data_copy['Unnamed: 0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uTW5q06CmW9C"
   },
   "outputs": [],
   "source": [
    "sm=SMOTE()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 70
    },
    "colab_type": "code",
    "id": "FIFl8kIxoWCF",
    "outputId": "770da65e-128f-44c5-b9d1-80368da7da77"
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'M'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-57-a67b7ab24725>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX_sample\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mY_sample\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_sample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/imblearn/base.py\u001b[0m in \u001b[0;36mfit_resample\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m     75\u001b[0m         \u001b[0mcheck_classification_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m         \u001b[0marrays_transformer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mArraysTransformer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 77\u001b[0;31m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbinarize_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_X_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     78\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m         self.sampling_strategy_ = check_sampling_strategy(\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/imblearn/base.py\u001b[0m in \u001b[0;36m_check_X_y\u001b[0;34m(self, X, y, accept_sparse)\u001b[0m\n\u001b[1;32m    132\u001b[0m             \u001b[0maccept_sparse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"csr\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"csc\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbinarize_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_target_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindicate_one_vs_all\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 134\u001b[0;31m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maccept_sparse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    135\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbinarize_y\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    753\u001b[0m                     \u001b[0mensure_min_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mensure_min_features\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    754\u001b[0m                     \u001b[0mwarn_on_dtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwarn_on_dtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 755\u001b[0;31m                     estimator=estimator)\n\u001b[0m\u001b[1;32m    756\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    757\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    529\u001b[0m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"unsafe\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    530\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 531\u001b[0;31m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    532\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    533\u001b[0m                 raise ValueError(\"Complex data not supported\\n\"\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/numpy/core/_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m     \"\"\"\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'M'"
     ]
    }
   ],
   "source": [
    "X_sample,Y_sample=sm.fit_sample(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "colab_type": "code",
    "id": "-yAD16z0q-pd",
    "outputId": "15ab815e-7dd3-4fa6-a391-851a24327c1f"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Y_sample' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-65-f4f9fe20f823>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcountplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY_sample\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'Y_sample' is not defined"
     ]
    }
   ],
   "source": [
    "sns.countplot(Y_sample)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dvwi38g-rmm7"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X_sample' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-38-90474eb11d32>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_sample\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mY_sample\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mstratify\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mY_sample\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m43\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'X_sample' is not defined"
     ]
    }
   ],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(X_sample,Y_sample,stratify=Y_sample,test_size=0.3,random_state=43)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iZxQtUrKrLWN"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'x_train' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-39-95bb1b84b4e3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m40\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mknn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mKNeighborsClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_neighbors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mknn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0my_pred\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mknn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0merror_rate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m!=\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'x_train' is not defined"
     ]
    }
   ],
   "source": [
    "error_rate=[]\n",
    "for i in range(1,40):\n",
    "    knn=KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(x_train,y_train)\n",
    "    y_pred=knn.predict(x_test)\n",
    "    error_rate.append(np.mean(y_pred!=y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 407
    },
    "colab_type": "code",
    "id": "NBAU4BRcsE9K",
    "outputId": "47587471-cae7-452b-a0f3-77095c35bdd6"
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (39,) and (0,)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-40-1b5296a7fe61>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m plt.plot(range(1,40),error_rate,color=\"blue\", linestyle=\"dashed\", marker=\"o\",\n\u001b[0;32m----> 3\u001b[0;31m  markerfacecolor=\"red\", markersize=10)\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Error Rate vs. K Value\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m40\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2793\u001b[0m     return gca().plot(\n\u001b[1;32m   2794\u001b[0m         *args, scalex=scalex, scaley=scaley, **({\"data\": data} if data\n\u001b[0;32m-> 2795\u001b[0;31m         is not None else {}), **kwargs)\n\u001b[0m\u001b[1;32m   2796\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2797\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1664\u001b[0m         \"\"\"\n\u001b[1;32m   1665\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_alias_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1666\u001b[0;31m         \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1667\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1668\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    223\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 225\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_next_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[0;34m(self, tup, kwargs)\u001b[0m\n\u001b[1;32m    389\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex_of\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 391\u001b[0;31m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_xy_from_xy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    392\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    393\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommand\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'plot'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_xy_from_xy\u001b[0;34m(self, x, y)\u001b[0m\n\u001b[1;32m    268\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    269\u001b[0m             raise ValueError(\"x and y must have same first dimension, but \"\n\u001b[0;32m--> 270\u001b[0;31m                              \"have shapes {} and {}\".format(x.shape, y.shape))\n\u001b[0m\u001b[1;32m    271\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    272\u001b[0m             raise ValueError(\"x and y can be no greater than 2-D, but have \"\n",
      "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (39,) and (0,)"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(1,40),error_rate,color=\"blue\", linestyle=\"dashed\", marker=\"o\",\n",
    " markerfacecolor=\"red\", markersize=10)\n",
    "plt.title(\"Error Rate vs. K Value\")\n",
    "plt.xticks(range(1,40))\n",
    "plt.ylabel(\"Error Rate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iKlhCqHhs-hu"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'x_train' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-41-a317f83b15a3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mknn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mKNeighborsClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_neighbors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mknn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0macc_knn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mknn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'x_train' is not defined"
     ]
    }
   ],
   "source": [
    "knn=KNeighborsClassifier(n_neighbors=1)\n",
    "knn.fit(x_train,y_train)\n",
    "acc_knn=knn.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "zgdcTR8AtR1Q",
    "outputId": "7084e1ba-c03a-44bd-c3c8-1f7e7e941f4e"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'acc_knn' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-42-384623a2d7b8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0macc_knn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'acc_knn' is not defined"
     ]
    }
   ],
   "source": [
    "acc_knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oaBkL3VRscaZ"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'x_train' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-43-857c06504985>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mrf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mRandomForestClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_estimators\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mrf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0my_pred\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0merror_rate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m!=\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'x_train' is not defined"
     ]
    }
   ],
   "source": [
    "error_rate=[]\n",
    "for i in range(50,300,10):\n",
    "    rf=RandomForestClassifier(n_estimators=i)\n",
    "    rf.fit(x_train,y_train)\n",
    "    y_pred=rf.predict(x_test)\n",
    "    error_rate.append(np.mean(y_pred!=y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "colab_type": "code",
    "id": "Q9WbgTu8szND",
    "outputId": "21fbdd64-213f-4319-b4c5-27e1c7526826"
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (25,) and (0,)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-44-74d8abaa94f8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m plt.plot(range(50,300,10),error_rate,color=\"blue\", linestyle=\"dashed\", marker=\"o\",\n\u001b[0;32m----> 3\u001b[0;31m  markerfacecolor=\"green\", markersize=10)\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Error Rate vs. n_estimators\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n_estimator\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2793\u001b[0m     return gca().plot(\n\u001b[1;32m   2794\u001b[0m         *args, scalex=scalex, scaley=scaley, **({\"data\": data} if data\n\u001b[0;32m-> 2795\u001b[0;31m         is not None else {}), **kwargs)\n\u001b[0m\u001b[1;32m   2796\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2797\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1664\u001b[0m         \"\"\"\n\u001b[1;32m   1665\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_alias_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1666\u001b[0;31m         \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1667\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1668\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    223\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 225\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_next_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[0;34m(self, tup, kwargs)\u001b[0m\n\u001b[1;32m    389\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex_of\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 391\u001b[0;31m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_xy_from_xy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    392\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    393\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommand\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'plot'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_xy_from_xy\u001b[0;34m(self, x, y)\u001b[0m\n\u001b[1;32m    268\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    269\u001b[0m             raise ValueError(\"x and y must have same first dimension, but \"\n\u001b[0;32m--> 270\u001b[0;31m                              \"have shapes {} and {}\".format(x.shape, y.shape))\n\u001b[0m\u001b[1;32m    271\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    272\u001b[0m             raise ValueError(\"x and y can be no greater than 2-D, but have \"\n",
      "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (25,) and (0,)"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(50,300,10),error_rate,color=\"blue\", linestyle=\"dashed\", marker=\"o\",\n",
    " markerfacecolor=\"green\", markersize=10)\n",
    "plt.title(\"Error Rate vs. n_estimators\")\n",
    "plt.xlabel(\"n_estimator\")\n",
    "plt.xticks(range(50,300,10))\n",
    "plt.ylabel(\"Error Rate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "POMt83j1tfQO"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'RandomForestClassifier' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-1f714017fe24>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mRandomForestClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_estimators\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m90\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mrf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0macc_rf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'RandomForestClassifier' is not defined"
     ]
    }
   ],
   "source": [
    "rf=RandomForestClassifier(n_estimators=90)\n",
    "rf.fit(x_train,y_train)\n",
    "acc_rf=rf.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "_hGp1wFitlxm",
    "outputId": "71ed64a2-a340-48ee-fdea-7cafed9192b9"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'acc_rf' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-62b4fc5cca8a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0macc_rf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'acc_rf' is not defined"
     ]
    }
   ],
   "source": [
    "acc_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "thyroid_csv.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}