thyroid_csv-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UlPfDU2UlEXm"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "I0Jd0rFklWTt"
   },
   "outputs": [],
   "source": [
    "data=pd.read_csv('hypothyroid.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>sick</th>\n",
       "      <th>tumor</th>\n",
       "      <th>lithium</th>\n",
       "      <th>goitre</th>\n",
       "      <th>TSH_measured</th>\n",
       "      <th>TSH</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",
       "      <th>0</th>\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>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>y</td>\n",
       "      <td>30</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",
       "      <th>1</th>\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>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>y</td>\n",
       "      <td>145</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",
       "      <th>2</th>\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>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>y</td>\n",
       "      <td>0</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",
       "      <th>3</th>\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>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>y</td>\n",
       "      <td>430</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",
       "      <th>4</th>\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>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>y</td>\n",
       "      <td>7.3</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",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0 Age Sex on_thyroxine  ... FTI_measured FTI TBG_measured TBG\n",
       "0  hypothyroid  72   M            f  ...            y  10            n   ?\n",
       "1  hypothyroid  15   F            t  ...            y  17            n   ?\n",
       "2  hypothyroid  24   M            f  ...            y   0            n   ?\n",
       "3  hypothyroid  24   F            f  ...            y   6            n   ?\n",
       "4  hypothyroid  77   M            f  ...            y  44            n   ?\n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "colab_type": "code",
    "id": "L5Uvlg_Iliq-",
    "outputId": "49b14cc8-676c-4b82-d81d-289ae73df579"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' 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-2-44246dc92f59>\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[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Unnamed: 0'\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;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "sns.countplot(data['Unnamed: 0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "itPF0EZbpCJL"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\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": 39,
   "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>sick</th>\n",
       "      <th>tumor</th>\n",
       "      <th>lithium</th>\n",
       "      <th>goitre</th>\n",
       "      <th>TSH_measured</th>\n",
       "      <th>TSH</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",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.48</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>145.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>430.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.04</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>57.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0   Age  Sex  on_thyroxine  ...  FTI_measured   FTI  TBG_measured  TBG\n",
       "0           0  72.0    0             0  ...             1  10.0             0    0\n",
       "1           0  15.0    1             1  ...             1  17.0             0    0\n",
       "2           0  24.0    0             0  ...             1   0.0             0    0\n",
       "3           0  24.0    1             0  ...             1   6.0             0    0\n",
       "4           0  77.0    0             0  ...             1  44.0             0    0\n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Rnj9rycTls2d"
   },
   "outputs": [],
   "source": [
    "x=data_copy.drop('Unnamed: 0',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "L2CC83OsoFPI"
   },
   "outputs": [],
   "source": [
    "y=data_copy['Unnamed: 0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uTW5q06CmW9C"
   },
   "outputs": [],
   "source": [
    "sm=SMOTE()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 70
    },
    "colab_type": "code",
    "id": "FIFl8kIxoWCF",
    "outputId": "770da65e-128f-44c5-b9d1-80368da7da77"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "X_sample,Y_sample=sm.fit_sample(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "colab_type": "code",
    "id": "-yAD16z0q-pd",
    "outputId": "15ab815e-7dd3-4fa6-a391-851a24327c1f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fccfb263390>"
      ]
     },
     "execution_count": 46,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAD4CAYAAAAdIcpQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ny7CQJHUZFpKkLsNCktRlWEiSuv4PvNARleEFOiMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(Y_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dvwi38g-rmm7"
   },
   "outputs": [],
   "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": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iZxQtUrKrLWN"
   },
   "outputs": [],
   "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": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 407
    },
    "colab_type": "code",
    "id": "NBAU4BRcsE9K",
    "outputId": "47587471-cae7-452b-a0f3-77095c35bdd6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Error Rate')"
      ]
     },
     "execution_count": 58,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAm0AAAF1CAYAAAC6Zt3VAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "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": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iKlhCqHhs-hu"
   },
   "outputs": [],
   "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": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "zgdcTR8AtR1Q",
    "outputId": "7084e1ba-c03a-44bd-c3c8-1f7e7e941f4e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9911504424778761"
      ]
     },
     "execution_count": 61,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oaBkL3VRscaZ"
   },
   "outputs": [],
   "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": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "colab_type": "code",
    "id": "Q9WbgTu8szND",
    "outputId": "21fbdd64-213f-4319-b4c5-27e1c7526826"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Error Rate')"
      ]
     },
     "execution_count": 63,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnoAAAGECAYAAACh2o34AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0DMMwzAMw4gpZugZhmEYhmHElP8PNjyb0EmY3wcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "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": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "POMt83j1tfQO"
   },
   "outputs": [],
   "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": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "_hGp1wFitlxm",
    "outputId": "71ed64a2-a340-48ee-fdea-7cafed9192b9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9933628318584071"
      ]
     },
     "execution_count": 65,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_rf"
   ]
  }
 ],
 "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
}