thyroid_detection.ipynb

{
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  {
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   "id": "4c6fd9cd-9e43-4d8d-9976-a809dd5c6117",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "86e0bac6-929d-487f-ab05-b943d2c279a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "file = open(\"dataset/thyroid.csv\")\n",
    "df = pd.read_csv(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fe2a0dd5-0a19-4964-ac60-067f21ce671c",
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    "df.drop(\"other\",axis=1,inplace=True)"
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   "id": "5742db9a-d00b-4854-9fe5-ab884d782777",
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    "feature_cols = [\"age\",\n",
    "                \"sex\",\n",
    "                \"on_thyroxine\",\n",
    "                \"query_on_thyroxine\",\n",
    "                \"on_antithyroid_medication\",\n",
    "                \"sick\",\n",
    "                \"pregnant\",\n",
    "                \"thyroid_surgery\",\n",
    "                \"I131_treatment\",\n",
    "                \"query_hypothyroid\",\n",
    "                \"query_hyperthyroid\",\n",
    "                \"lithium\",\n",
    "                \"goitre\",\n",
    "                \"tumor\",\n",
    "                \"hypopituitary\",\n",
    "                \"psych\",\n",
    "                \"TSH measured\",\n",
    "                \"TSH\",\n",
    "                \"T3_measured\",\n",
    "                \"T3\",\n",
    "                \"TT4_measured\",\n",
    "                \"TT4\",\n",
    "                \"T4U_measured\",\n",
    "                \"T4U\",\n",
    "                \"FTI_measured\",\n",
    "                \"FTI\",\n",
    "                \"TBG_measured\",\n",
    "                \"TBG\",\n",
    "               \"target\"]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c9afbd0e-15f8-45bd-9cc6-80e8acaa6824",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = feature_cols"
   ]
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       "      <td>?</td>\n",
       "      <td>t</td>\n",
       "      <td>36</td>\n",
       "      <td>S[840803047]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>F</td>\n",
       "      <td>f</td>\n",
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       "      <td>f</td>\n",
       "      <td>f</td>\n",
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       "      <td>?</td>\n",
       "      <td>f</td>\n",
       "      <td>?</td>\n",
       "      <td>t</td>\n",
       "      <td>26</td>\n",
       "      <td>-[840803048]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>9166</th>\n",
       "      <td>56</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>...</td>\n",
       "      <td>?</td>\n",
       "      <td>t</td>\n",
       "      <td>64</td>\n",
       "      <td>t</td>\n",
       "      <td>0.83</td>\n",
       "      <td>t</td>\n",
       "      <td>77</td>\n",
       "      <td>f</td>\n",
       "      <td>?</td>\n",
       "      <td>-[870119022]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9167</th>\n",
       "      <td>22</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>...</td>\n",
       "      <td>?</td>\n",
       "      <td>t</td>\n",
       "      <td>91</td>\n",
       "      <td>t</td>\n",
       "      <td>0.92</td>\n",
       "      <td>t</td>\n",
       "      <td>99</td>\n",
       "      <td>f</td>\n",
       "      <td>?</td>\n",
       "      <td>-[870119023]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9168</th>\n",
       "      <td>69</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>...</td>\n",
       "      <td>?</td>\n",
       "      <td>t</td>\n",
       "      <td>113</td>\n",
       "      <td>t</td>\n",
       "      <td>1.27</td>\n",
       "      <td>t</td>\n",
       "      <td>89</td>\n",
       "      <td>f</td>\n",
       "      <td>?</td>\n",
       "      <td>I[870119025]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9169</th>\n",
       "      <td>47</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>...</td>\n",
       "      <td>?</td>\n",
       "      <td>t</td>\n",
       "      <td>75</td>\n",
       "      <td>t</td>\n",
       "      <td>0.85</td>\n",
       "      <td>t</td>\n",
       "      <td>88</td>\n",
       "      <td>f</td>\n",
       "      <td>?</td>\n",
       "      <td>-[870119027]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9170</th>\n",
       "      <td>31</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>t</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>t</td>\n",
       "      <td>66</td>\n",
       "      <td>t</td>\n",
       "      <td>1.02</td>\n",
       "      <td>t</td>\n",
       "      <td>65</td>\n",
       "      <td>f</td>\n",
       "      <td>?</td>\n",
       "      <td>-[870119035]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9171 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age sex on_thyroxine query_on_thyroxine on_antithyroid_medication sick  \\\n",
       "0      29   F            f                  f                         f    f   \n",
       "1      41   F            f                  f                         f    f   \n",
       "2      36   F            f                  f                         f    f   \n",
       "3      32   F            f                  f                         f    f   \n",
       "4      60   F            f                  f                         f    f   \n",
       "...   ...  ..          ...                ...                       ...  ...   \n",
       "9166   56   M            f                  f                         f    f   \n",
       "9167   22   M            f                  f                         f    f   \n",
       "9168   69   M            f                  f                         f    f   \n",
       "9169   47   F            f                  f                         f    f   \n",
       "9170   31   M            f                  f                         f    f   \n",
       "\n",
       "     pregnant thyroid_surgery I131_treatment query_hypothyroid  ...   T3  \\\n",
       "0           f               f              f                 f  ...  1.9   \n",
       "1           f               f              f                 f  ...    ?   \n",
       "2           f               f              f                 f  ...    ?   \n",
       "3           f               f              f                 f  ...    ?   \n",
       "4           f               f              f                 f  ...    ?   \n",
       "...       ...             ...            ...               ...  ...  ...   \n",
       "9166        f               f              f                 f  ...    ?   \n",
       "9167        f               f              f                 f  ...    ?   \n",
       "9168        f               f              f                 f  ...    ?   \n",
       "9169        f               f              f                 f  ...    ?   \n",
       "9170        f               f              f                 t  ...    ?   \n",
       "\n",
       "     TT4_measured  TT4 T4U_measured   T4U FTI_measured FTI TBG_measured TBG  \\\n",
       "0               t  128            f     ?            f   ?            f   ?   \n",
       "1               f    ?            f     ?            f   ?            t  11   \n",
       "2               f    ?            f     ?            f   ?            t  26   \n",
       "3               f    ?            f     ?            f   ?            t  36   \n",
       "4               f    ?            f     ?            f   ?            t  26   \n",
       "...           ...  ...          ...   ...          ...  ..          ...  ..   \n",
       "9166            t   64            t  0.83            t  77            f   ?   \n",
       "9167            t   91            t  0.92            t  99            f   ?   \n",
       "9168            t  113            t  1.27            t  89            f   ?   \n",
       "9169            t   75            t  0.85            t  88            f   ?   \n",
       "9170            t   66            t  1.02            t  65            f   ?   \n",
       "\n",
       "            target  \n",
       "0     -[840801014]  \n",
       "1     -[840801042]  \n",
       "2     -[840803046]  \n",
       "3     S[840803047]  \n",
       "4     -[840803048]  \n",
       "...            ...  \n",
       "9166  -[870119022]  \n",
       "9167  -[870119023]  \n",
       "9168  I[870119025]  \n",
       "9169  -[870119027]  \n",
       "9170  -[870119035]  \n",
       "\n",
       "[9171 rows x 29 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "417e36dc-ed0f-43d7-95ad-feb417c25b99",
   "metadata": {},
   "source": [
    "# Splitting target\n",
    "\n",
    "## Now we can check that the target columns as many categorial names with some indicate numbers so going to split with the respective features \n",
    "\n",
    "### thyroid.names file content\n",
    "\n",
    "The diagnosis consists of a string of letters indicating diagnosed conditions.\n",
    "A diagnosis \"-\" indicates no condition requiring comment.  A diagnosis of the\n",
    "form \"X|Y\" is interpreted as \"consistent with X, but more likely Y\".  The\n",
    "conditions are divided into groups where each group corresponds to a class of\n",
    "comments.\n",
    "\n",
    "\t\tLetter\tDiagnosis\n",
    "\t\t------\t---------\n",
    "\n",
    "\thyperthyroid conditions:\n",
    "\n",
    "\t\tA\thyperthyroid\n",
    "\t\tB\tT3 toxic\n",
    "\t\tC\ttoxic goitre\n",
    "\t\tD\tsecondary toxic\n",
    "\n",
    "\thypothyroid conditions:\n",
    "\n",
    "\t\tE\thypothyroid\n",
    "\t\tF\tprimary hypothyroid\n",
    "\t\tG\tcompensated hypothyroid\n",
    "\t\tH\tsecondary hypothyroid\n",
    "\n",
    "\tbinding protein:\n",
    "\n",
    "\t\tI\tincreased binding protein\n",
    "\t\tJ\tdecreased binding protein\n",
    "\n",
    "\tgeneral health:\n",
    "\n",
    "\t\tK\tconcurrent non-thyroidal illness\n",
    "\n",
    "\treplacement therapy:\n",
    "\n",
    "\t\tL\tconsistent with replacement therapy\n",
    "\t\tM\tunderreplaced\n",
    "\t\tN\toverreplaced\n",
    "\n",
    "\tantithyroid treatment:\n",
    "\n",
    "\t\tO\tantithyroid drugs\n",
    "\t\tP\tI131 treatment\n",
    "\t\tQ\tsurgery\n",
    "\n",
    "\tmiscellaneous:\n",
    "\n",
    "\t\tR\tdiscordant assay results\n",
    "\t\tS\televated TBG\n",
    "\t\tT\televated thyroid hormones\n",
    "\n",
    "In experiments with an earlier version of this archive, decision trees were\n",
    "derived for the most frequent classes of comments, namely\n",
    "\n",
    "\thyperthyroid conditions (A, B, C, D)\n",
    "\thypothyroid conditions (E, F, G, H)\n",
    "\tbinding protein (I, J)\n",
    "\tgeneral health (K)\n",
    "\treplacement therapy (L, M, N)\n",
    "\tdiscordant results (R)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "985dbc4e-6bba-4b2a-b13d-f450211b8b1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "target = df.target\n",
    "create = target.str.split('([A-Za-z]+)', expand=True)\n",
    "create = create[1]\n",
    "target = create.replace({None:'Z'}) #here z is none type\n",
    "df.target = target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1a15fd3b-70d3-48b5-811d-dd18851baa1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Z', 'S', 'F', 'AK', 'R', 'I', 'M', 'N', 'G', 'K', 'A', 'KJ', 'L',\n",
       "       'MK', 'Q', 'J', 'C', 'O', 'LJ', 'H', 'D', 'GK', 'MI', 'P', 'FK',\n",
       "       'B', 'GI', 'GKJ', 'OI', 'E'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.target.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "752646bc-ed13-41d3-84a6-fb6be74918d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>age</th>\n",
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       "      <td>f</td>\n",
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       "      <td>?</td>\n",
       "      <td>t</td>\n",
       "      <td>91</td>\n",
       "      <td>t</td>\n",
       "      <td>0.92</td>\n",
       "      <td>t</td>\n",
       "      <td>99</td>\n",
       "      <td>f</td>\n",
       "      <td>?</td>\n",
       "      <td>Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9168</th>\n",
       "      <td>69</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>...</td>\n",
       "      <td>?</td>\n",
       "      <td>t</td>\n",
       "      <td>113</td>\n",
       "      <td>t</td>\n",
       "      <td>1.27</td>\n",
       "      <td>t</td>\n",
       "      <td>89</td>\n",
       "      <td>f</td>\n",
       "      <td>?</td>\n",
       "      <td>I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9169</th>\n",
       "      <td>47</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>...</td>\n",
       "      <td>?</td>\n",
       "      <td>t</td>\n",
       "      <td>75</td>\n",
       "      <td>t</td>\n",
       "      <td>0.85</td>\n",
       "      <td>t</td>\n",
       "      <td>88</td>\n",
       "      <td>f</td>\n",
       "      <td>?</td>\n",
       "      <td>Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9170</th>\n",
       "      <td>31</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>t</td>\n",
       "      <td>...</td>\n",
       "      <td>?</td>\n",
       "      <td>t</td>\n",
       "      <td>66</td>\n",
       "      <td>t</td>\n",
       "      <td>1.02</td>\n",
       "      <td>t</td>\n",
       "      <td>65</td>\n",
       "      <td>f</td>\n",
       "      <td>?</td>\n",
       "      <td>Z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9171 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age sex on_thyroxine query_on_thyroxine on_antithyroid_medication sick  \\\n",
       "0      29   F            f                  f                         f    f   \n",
       "1      41   F            f                  f                         f    f   \n",
       "2      36   F            f                  f                         f    f   \n",
       "3      32   F            f                  f                         f    f   \n",
       "4      60   F            f                  f                         f    f   \n",
       "...   ...  ..          ...                ...                       ...  ...   \n",
       "9166   56   M            f                  f                         f    f   \n",
       "9167   22   M            f                  f                         f    f   \n",
       "9168   69   M            f                  f                         f    f   \n",
       "9169   47   F            f                  f                         f    f   \n",
       "9170   31   M            f                  f                         f    f   \n",
       "\n",
       "     pregnant thyroid_surgery I131_treatment query_hypothyroid  ...   T3  \\\n",
       "0           f               f              f                 f  ...  1.9   \n",
       "1           f               f              f                 f  ...    ?   \n",
       "2           f               f              f                 f  ...    ?   \n",
       "3           f               f              f                 f  ...    ?   \n",
       "4           f               f              f                 f  ...    ?   \n",
       "...       ...             ...            ...               ...  ...  ...   \n",
       "9166        f               f              f                 f  ...    ?   \n",
       "9167        f               f              f                 f  ...    ?   \n",
       "9168        f               f              f                 f  ...    ?   \n",
       "9169        f               f              f                 f  ...    ?   \n",
       "9170        f               f              f                 t  ...    ?   \n",
       "\n",
       "     TT4_measured  TT4 T4U_measured   T4U FTI_measured FTI TBG_measured TBG  \\\n",
       "0               t  128            f     ?            f   ?            f   ?   \n",
       "1               f    ?            f     ?            f   ?            t  11   \n",
       "2               f    ?            f     ?            f   ?            t  26   \n",
       "3               f    ?            f     ?            f   ?            t  36   \n",
       "4               f    ?            f     ?            f   ?            t  26   \n",
       "...           ...  ...          ...   ...          ...  ..          ...  ..   \n",
       "9166            t   64            t  0.83            t  77            f   ?   \n",
       "9167            t   91            t  0.92            t  99            f   ?   \n",
       "9168            t  113            t  1.27            t  89            f   ?   \n",
       "9169            t   75            t  0.85            t  88            f   ?   \n",
       "9170            t   66            t  1.02            t  65            f   ?   \n",
       "\n",
       "     target  \n",
       "0         Z  \n",
       "1         Z  \n",
       "2         Z  \n",
       "3         S  \n",
       "4         Z  \n",
       "...     ...  \n",
       "9166      Z  \n",
       "9167      Z  \n",
       "9168      I  \n",
       "9169      Z  \n",
       "9170      Z  \n",
       "\n",
       "[9171 rows x 29 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64a72611-3e0b-4cba-9f2f-bd0ab8c4e1ec",
   "metadata": {},
   "source": [
    "### Now we want to impute the null values but this case the null values are marked as in '?' so we can do some tricks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c066acef-a086-4011-afbc-3b44eecd1953",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.replace(['?'],np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5a080325-b3ed-47be-8835-5c1528273ae9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                             0\n",
       "sex                           307\n",
       "on_thyroxine                    0\n",
       "query_on_thyroxine              0\n",
       "on_antithyroid_medication       0\n",
       "sick                            0\n",
       "pregnant                        0\n",
       "thyroid_surgery                 0\n",
       "I131_treatment                  0\n",
       "query_hypothyroid               0\n",
       "query_hyperthyroid              0\n",
       "lithium                         0\n",
       "goitre                          0\n",
       "tumor                           0\n",
       "hypopituitary                   0\n",
       "psych                           0\n",
       "TSH measured                    0\n",
       "TSH                           842\n",
       "T3_measured                     0\n",
       "T3                           2603\n",
       "TT4_measured                    0\n",
       "TT4                           441\n",
       "T4U_measured                    0\n",
       "T4U                           808\n",
       "FTI_measured                    0\n",
       "FTI                           801\n",
       "TBG_measured                    0\n",
       "TBG                          8822\n",
       "target                          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cea4a044-8562-4bb7-a690-a2b89668d698",
   "metadata": {},
   "outputs": [],
   "source": [
    "# here we can see the TBG has more null observations it will tremendously occur problem so we can remove and some of the other \n",
    "# feautre rows which is not useful\n",
    "\n",
    "df.drop(['TBG_measured','TBG','T3_measured','TSH measured','TT4_measured','T4U_measured','FTI_measured'],axis=1,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fb8f3ec9-a7fe-4897-9bc3-1d62a4ace550",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>age</th>\n",
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       "      <th>9168</th>\n",
       "      <td>69</td>\n",
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       "      <td>66</td>\n",
       "      <td>1.02</td>\n",
       "      <td>65</td>\n",
       "      <td>Z</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>9171 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age sex on_thyroxine query_on_thyroxine on_antithyroid_medication sick  \\\n",
       "0      29   F            f                  f                         f    f   \n",
       "1      41   F            f                  f                         f    f   \n",
       "2      36   F            f                  f                         f    f   \n",
       "3      32   F            f                  f                         f    f   \n",
       "4      60   F            f                  f                         f    f   \n",
       "...   ...  ..          ...                ...                       ...  ...   \n",
       "9166   56   M            f                  f                         f    f   \n",
       "9167   22   M            f                  f                         f    f   \n",
       "9168   69   M            f                  f                         f    f   \n",
       "9169   47   F            f                  f                         f    f   \n",
       "9170   31   M            f                  f                         f    f   \n",
       "\n",
       "     pregnant thyroid_surgery I131_treatment query_hypothyroid  ... goitre  \\\n",
       "0           f               f              f                 f  ...      f   \n",
       "1           f               f              f                 f  ...      f   \n",
       "2           f               f              f                 f  ...      f   \n",
       "3           f               f              f                 f  ...      f   \n",
       "4           f               f              f                 f  ...      f   \n",
       "...       ...             ...            ...               ...  ...    ...   \n",
       "9166        f               f              f                 f  ...      f   \n",
       "9167        f               f              f                 f  ...      f   \n",
       "9168        f               f              f                 f  ...      f   \n",
       "9169        f               f              f                 f  ...      f   \n",
       "9170        f               f              f                 t  ...      f   \n",
       "\n",
       "     tumor hypopituitary psych  TSH   T3  TT4   T4U  FTI target  \n",
       "0        f             f     f  1.6  1.9  128   NaN  NaN      Z  \n",
       "1        f             f     f  NaN  NaN  NaN   NaN  NaN      Z  \n",
       "2        f             f     f  NaN  NaN  NaN   NaN  NaN      Z  \n",
       "3        f             f     f  NaN  NaN  NaN   NaN  NaN      S  \n",
       "4        f             f     f  NaN  NaN  NaN   NaN  NaN      Z  \n",
       "...    ...           ...   ...  ...  ...  ...   ...  ...    ...  \n",
       "9166     f             f     f  NaN  NaN   64  0.83   77      Z  \n",
       "9167     f             f     f  NaN  NaN   91  0.92   99      Z  \n",
       "9168     f             f     f  NaN  NaN  113  1.27   89      I  \n",
       "9169     f             f     f  NaN  NaN   75  0.85   88      Z  \n",
       "9170     f             f     f  NaN  NaN   66  1.02   65      Z  \n",
       "\n",
       "[9171 rows x 22 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d9966a71-7252-4296-822d-0b97f90aae46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                             0\n",
       "sex                           307\n",
       "on_thyroxine                    0\n",
       "query_on_thyroxine              0\n",
       "on_antithyroid_medication       0\n",
       "sick                            0\n",
       "pregnant                        0\n",
       "thyroid_surgery                 0\n",
       "I131_treatment                  0\n",
       "query_hypothyroid               0\n",
       "query_hyperthyroid              0\n",
       "lithium                         0\n",
       "goitre                          0\n",
       "tumor                           0\n",
       "hypopituitary                   0\n",
       "psych                           0\n",
       "TSH                           842\n",
       "T3                           2603\n",
       "TT4                           441\n",
       "T4U                           808\n",
       "FTI                           801\n",
       "target                          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "41a4427e-e451-41bb-a2a6-054f1f1985d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sex.replace({'F':2,'M':1},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1eaae85c-978a-45d9-ac79-a5df29447b4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "round_Values = round(df.sex.mean())\n",
    "df.sex.fillna(round_Values,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fa91e37a-0e97-482a-b16b-be80085900b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2., 1.])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sex.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e0424ac1-840f-4b3c-88df-8aa8d0329929",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                             0\n",
       "sex                             0\n",
       "on_thyroxine                    0\n",
       "query_on_thyroxine              0\n",
       "on_antithyroid_medication       0\n",
       "sick                            0\n",
       "pregnant                        0\n",
       "thyroid_surgery                 0\n",
       "I131_treatment                  0\n",
       "query_hypothyroid               0\n",
       "query_hyperthyroid              0\n",
       "lithium                         0\n",
       "goitre                          0\n",
       "tumor                           0\n",
       "hypopituitary                   0\n",
       "psych                           0\n",
       "TSH                           842\n",
       "T3                           2603\n",
       "TT4                           441\n",
       "T4U                           808\n",
       "FTI                           801\n",
       "target                          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f6742745-ac78-4fa0-a04b-dd7cabaa1860",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we will impute the null values with knn imputer\n",
    "from sklearn.impute import KNNImputer\n",
    "knnimp = KNNImputer(n_neighbors=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "adf5dac5-0cca-44b9-91c3-49fe25a4f872",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['TSH','T3','TT4','T4U','FTI']\n",
    "for i in cols:\n",
    "    df[i] = knnimp.fit_transform(df[[i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2ada0a16-04f3-45a5-9656-5cc71fd824be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                          0\n",
       "sex                          0\n",
       "on_thyroxine                 0\n",
       "query_on_thyroxine           0\n",
       "on_antithyroid_medication    0\n",
       "sick                         0\n",
       "pregnant                     0\n",
       "thyroid_surgery              0\n",
       "I131_treatment               0\n",
       "query_hypothyroid            0\n",
       "query_hyperthyroid           0\n",
       "lithium                      0\n",
       "goitre                       0\n",
       "tumor                        0\n",
       "hypopituitary                0\n",
       "psych                        0\n",
       "TSH                          0\n",
       "T3                           0\n",
       "TT4                          0\n",
       "T4U                          0\n",
       "FTI                          0\n",
       "target                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum() # now we can see there is no null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "226b5a03-e097-4d3f-b236-d9a87f30197b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9171 entries, 0 to 9170\n",
      "Data columns (total 22 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   age                        9171 non-null   int64  \n",
      " 1   sex                        9171 non-null   float64\n",
      " 2   on_thyroxine               9171 non-null   object \n",
      " 3   query_on_thyroxine         9171 non-null   object \n",
      " 4   on_antithyroid_medication  9171 non-null   object \n",
      " 5   sick                       9171 non-null   object \n",
      " 6   pregnant                   9171 non-null   object \n",
      " 7   thyroid_surgery            9171 non-null   object \n",
      " 8   I131_treatment             9171 non-null   object \n",
      " 9   query_hypothyroid          9171 non-null   object \n",
      " 10  query_hyperthyroid         9171 non-null   object \n",
      " 11  lithium                    9171 non-null   object \n",
      " 12  goitre                     9171 non-null   object \n",
      " 13  tumor                      9171 non-null   object \n",
      " 14  hypopituitary              9171 non-null   object \n",
      " 15  psych                      9171 non-null   object \n",
      " 16  TSH                        9171 non-null   float64\n",
      " 17  T3                         9171 non-null   float64\n",
      " 18  TT4                        9171 non-null   float64\n",
      " 19  T4U                        9171 non-null   float64\n",
      " 20  FTI                        9171 non-null   float64\n",
      " 21  target                     9171 non-null   object \n",
      "dtypes: float64(6), int64(1), object(15)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20ad0a4d-1e26-4d1c-94fb-e0a07f480945",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7cca2dd6-31d5-4147-be6e-71d412929bfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "sns.heatmap(df.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b28fe9ab-1d55-472a-a46c-fbb702a25530",
   "metadata": {},
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>TSH</th>\n",
       "      <th>T3</th>\n",
       "      <th>TT4</th>\n",
       "      <th>T4U</th>\n",
       "      <th>FTI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.014175</td>\n",
       "      <td>-0.003979</td>\n",
       "      <td>0.003693</td>\n",
       "      <td>0.004122</td>\n",
       "      <td>-0.002045</td>\n",
       "      <td>0.004108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>-0.014175</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.036723</td>\n",
       "      <td>0.048890</td>\n",
       "      <td>0.150479</td>\n",
       "      <td>0.214723</td>\n",
       "      <td>0.030149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TSH</th>\n",
       "      <td>-0.003979</td>\n",
       "      <td>0.036723</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.136613</td>\n",
       "      <td>-0.264756</td>\n",
       "      <td>0.072962</td>\n",
       "      <td>-0.257901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T3</th>\n",
       "      <td>0.003693</td>\n",
       "      <td>0.048890</td>\n",
       "      <td>-0.136613</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.471792</td>\n",
       "      <td>0.281336</td>\n",
       "      <td>0.312401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TT4</th>\n",
       "      <td>0.004122</td>\n",
       "      <td>0.150479</td>\n",
       "      <td>-0.264756</td>\n",
       "      <td>0.471792</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.362280</td>\n",
       "      <td>0.738279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T4U</th>\n",
       "      <td>-0.002045</td>\n",
       "      <td>0.214723</td>\n",
       "      <td>0.072962</td>\n",
       "      <td>0.281336</td>\n",
       "      <td>0.362280</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.238927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FTI</th>\n",
       "      <td>0.004108</td>\n",
       "      <td>0.030149</td>\n",
       "      <td>-0.257901</td>\n",
       "      <td>0.312401</td>\n",
       "      <td>0.738279</td>\n",
       "      <td>-0.238927</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          age       sex       TSH        T3       TT4       T4U       FTI\n",
       "age  1.000000 -0.014175 -0.003979  0.003693  0.004122 -0.002045  0.004108\n",
       "sex -0.014175  1.000000  0.036723  0.048890  0.150479  0.214723  0.030149\n",
       "TSH -0.003979  0.036723  1.000000 -0.136613 -0.264756  0.072962 -0.257901\n",
       "T3   0.003693  0.048890 -0.136613  1.000000  0.471792  0.281336  0.312401\n",
       "TT4  0.004122  0.150479 -0.264756  0.471792  1.000000  0.362280  0.738279\n",
       "T4U -0.002045  0.214723  0.072962  0.281336  0.362280  1.000000 -0.238927\n",
       "FTI  0.004108  0.030149 -0.257901  0.312401  0.738279 -0.238927  1.000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e55c72d1-380d-4261-8de9-1ca4f5f90cc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can't find corr for all variable because some of the features are in categorial object so we want to do label encoder\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d4445b99-2f49-4f57-9aa8-f0dd95845444",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = df.select_dtypes(include=['object'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6cd74792-2a5e-4801-91fe-115a297c8b7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in cols.columns:\n",
    "    try:\n",
    "        df[i] = le.fit_transform(df[i])\n",
    "    except:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f8dc6696-b17b-4605-9e59-6cc2bb16ff0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TT4\n"
     ]
    }
   ],
   "source": [
    "# now we can see their is correlation in some features\n",
    "for a in range(len(df.corr())):\n",
    "    for b in range(a):\n",
    "        if((df.corr().iloc[a,b]) >= 0.7):\n",
    "            print(df.corr().columns[b])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a8c51277-ea0a-4837-9653-d7e23628cbed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# so TT4 has high correlation among all we can remove\n",
    "\n",
    "df.drop('TT4',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "eceebc9f-aeb9-4219-96e9-8afc97ea3c43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(bins=25,figsize=(20,20));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52445d60-9905-4102-805c-2a4be80901e2",
   "metadata": {},
   "source": [
    "### Now we can see the data normally distributed in some features and some are categorigal now we have to normalize the values \n",
    "### because most of the values lies between 0 to 500 in x-axis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15950808-985c-43f1-847c-4e11f2d51957",
   "metadata": {},
   "source": [
    "# preprocessing techniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "654f9ac0-0508-4771-8036-9362390215e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# X and Y split\n",
    "\n",
    "X = df.drop('target',axis=1)\n",
    "y = df.target\n",
    "df2 = X # for on-going process without PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "32a09bd7-3013-41b8-92ce-6f1d47717534",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([29, 28,  6,  1, 27, 13, 19, 22,  8, 15,  0, 16, 17, 21, 26, 14,  3,\n",
       "       23, 18, 12,  4, 10, 20, 25,  7,  2,  9, 11, 24,  5])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.unique() # we can see there is 29 types are present => 29 categorigal values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3aade79-0d46-4326-9691-c7acf7030358",
   "metadata": {},
   "source": [
    "# PCA Technique\n",
    "\n",
    "### First we use PCA then see the result then we move to normal modeling(without PCA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "508f0c85-b25d-4aa3-a8c9-1f573d129050",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e7c7f4d4-358f-4d9e-9d0f-3012b2b5896b",
   "metadata": {},
   "outputs": [],
   "source": [
    "v = pca.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ec0e0faf-1756-4a7e-90e3-ce6d39b2ee42",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_pca = pd.DataFrame(data = v, columns = ['component_1', 'component_2', 'component_3', 'component_4', 'component_5', 'component_6', 'component_7', 'component_8', 'component_9', 'component_10'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f2fabc28-1486-42c1-bb2d-3d533426b17d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>component_1</th>\n",
       "      <th>component_2</th>\n",
       "      <th>component_3</th>\n",
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       "      <td>0.016456</td>\n",
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       "      <td>-0.037736</td>\n",
       "      <td>-0.052238</td>\n",
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       "      <td>0.016418</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>9166</th>\n",
       "      <td>-17.565725</td>\n",
       "      <td>-35.815558</td>\n",
       "      <td>-7.717545</td>\n",
       "      <td>0.204036</td>\n",
       "      <td>0.670809</td>\n",
       "      <td>0.039462</td>\n",
       "      <td>0.841271</td>\n",
       "      <td>-0.465404</td>\n",
       "      <td>0.039248</td>\n",
       "      <td>-0.003876</td>\n",
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       "    <tr>\n",
       "      <th>9167</th>\n",
       "      <td>-51.562695</td>\n",
       "      <td>-14.304689</td>\n",
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       "      <td>0.706841</td>\n",
       "      <td>-0.003652</td>\n",
       "      <td>-0.033362</td>\n",
       "      <td>-0.006760</td>\n",
       "      <td>-0.082480</td>\n",
       "      <td>-0.059033</td>\n",
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       "      <th>9168</th>\n",
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       "      <td>0.670897</td>\n",
       "      <td>-0.034908</td>\n",
       "      <td>-0.036777</td>\n",
       "      <td>-0.016309</td>\n",
       "      <td>-0.078404</td>\n",
       "      <td>-0.052621</td>\n",
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       "      <th>9169</th>\n",
       "      <td>-26.564217</td>\n",
       "      <td>-25.061240</td>\n",
       "      <td>-5.400598</td>\n",
       "      <td>0.142657</td>\n",
       "      <td>-0.264609</td>\n",
       "      <td>0.156261</td>\n",
       "      <td>-0.103200</td>\n",
       "      <td>-0.012638</td>\n",
       "      <td>-0.039070</td>\n",
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       "      <td>0.217002</td>\n",
       "      <td>0.622046</td>\n",
       "      <td>-0.217718</td>\n",
       "      <td>0.424985</td>\n",
       "      <td>0.849245</td>\n",
       "      <td>-0.045017</td>\n",
       "      <td>-0.073067</td>\n",
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       "<p>9171 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      component_1  component_2  component_3  component_4  component_5  \\\n",
       "0      -44.560401     0.768306    -3.539707    -0.060346    -0.278302   \n",
       "1      -32.560678     0.005264    -0.001276     0.074488    -0.307936   \n",
       "2      -37.560681     0.005585    -0.001597     0.016450    -0.269516   \n",
       "3      -41.560681     0.006190    -0.001784     0.016456    -0.269494   \n",
       "4      -13.560682     0.001958    -0.000475     0.016418    -0.269653   \n",
       "...           ...          ...          ...          ...          ...   \n",
       "9166   -17.565725   -35.815558    -7.717545     0.204036     0.670809   \n",
       "9167   -51.562695   -14.304689    -3.086385     0.035777     0.706841   \n",
       "9168    -4.564074   -24.087764    -5.190078     0.128057     0.670897   \n",
       "9169   -26.564217   -25.061240    -5.400598     0.142657    -0.264609   \n",
       "9170   -42.567384   -47.543360   -10.245334     0.217002     0.622046   \n",
       "\n",
       "      component_6  component_7  component_8  component_9  component_10  \n",
       "0        0.186705    -0.101526    -0.008343    -0.036863     -0.054012  \n",
       "1        0.256393     0.767614    -0.459433     0.084921      0.004770  \n",
       "2        0.186457    -0.105393    -0.006417    -0.037733     -0.052237  \n",
       "3        0.186463    -0.105379    -0.006425    -0.037736     -0.052238  \n",
       "4        0.186422    -0.105475    -0.006369    -0.037716     -0.052226  \n",
       "...           ...          ...          ...          ...           ...  \n",
       "9166     0.039462     0.841271    -0.465404     0.039248     -0.003876  \n",
       "9167    -0.003652    -0.033362    -0.006760    -0.082480     -0.059033  \n",
       "9168    -0.034908    -0.036777    -0.016309    -0.078404     -0.052621  \n",
       "9169     0.156261    -0.103200    -0.012638    -0.039070     -0.054794  \n",
       "9170    -0.217718     0.424985     0.849245    -0.045017     -0.073067  \n",
       "\n",
       "[9171 rows x 10 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "63977267-d543-4637-94b9-c6275ded4d83",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "232b366d-cd3a-4ec7-a43f-1fe67149393a",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in X_pca.columns:\n",
    "    X_pca[i] = scaler.fit_transform(X_pca[[i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "038d0a22-ca5a-48e5-a8ec-dd8859e31cc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_pca.hist(bins=25,figsize=(20,20));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "26b5459c-1705-4274-a1b9-fca675bfca5a",
   "metadata": {},
   "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>component_1</th>\n",
       "      <th>component_2</th>\n",
       "      <th>component_3</th>\n",
       "      <th>component_4</th>\n",
       "      <th>component_5</th>\n",
       "      <th>component_6</th>\n",
       "      <th>component_7</th>\n",
       "      <th>component_8</th>\n",
       "      <th>component_9</th>\n",
       "      <th>component_10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000427</td>\n",
       "      <td>0.222139</td>\n",
       "      <td>0.041115</td>\n",
       "      <td>0.232067</td>\n",
       "      <td>0.186966</td>\n",
       "      <td>0.560488</td>\n",
       "      <td>0.248026</td>\n",
       "      <td>0.418244</td>\n",
       "      <td>0.206391</td>\n",
       "      <td>0.048862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000611</td>\n",
       "      <td>0.221348</td>\n",
       "      <td>0.047978</td>\n",
       "      <td>0.239986</td>\n",
       "      <td>0.171936</td>\n",
       "      <td>0.588608</td>\n",
       "      <td>0.701865</td>\n",
       "      <td>0.147536</td>\n",
       "      <td>0.293047</td>\n",
       "      <td>0.095867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000534</td>\n",
       "      <td>0.221348</td>\n",
       "      <td>0.047977</td>\n",
       "      <td>0.236577</td>\n",
       "      <td>0.191421</td>\n",
       "      <td>0.560389</td>\n",
       "      <td>0.246007</td>\n",
       "      <td>0.419400</td>\n",
       "      <td>0.205773</td>\n",
       "      <td>0.050282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000473</td>\n",
       "      <td>0.221349</td>\n",
       "      <td>0.047977</td>\n",
       "      <td>0.236578</td>\n",
       "      <td>0.191433</td>\n",
       "      <td>0.560391</td>\n",
       "      <td>0.246014</td>\n",
       "      <td>0.419395</td>\n",
       "      <td>0.205770</td>\n",
       "      <td>0.050281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000900</td>\n",
       "      <td>0.221344</td>\n",
       "      <td>0.047979</td>\n",
       "      <td>0.236575</td>\n",
       "      <td>0.191352</td>\n",
       "      <td>0.560374</td>\n",
       "      <td>0.245964</td>\n",
       "      <td>0.419429</td>\n",
       "      <td>0.205785</td>\n",
       "      <td>0.050290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9166</th>\n",
       "      <td>0.000839</td>\n",
       "      <td>0.184174</td>\n",
       "      <td>0.033012</td>\n",
       "      <td>0.247595</td>\n",
       "      <td>0.668313</td>\n",
       "      <td>0.501074</td>\n",
       "      <td>0.740327</td>\n",
       "      <td>0.143953</td>\n",
       "      <td>0.260548</td>\n",
       "      <td>0.088953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9167</th>\n",
       "      <td>0.000321</td>\n",
       "      <td>0.206497</td>\n",
       "      <td>0.041994</td>\n",
       "      <td>0.237712</td>\n",
       "      <td>0.686587</td>\n",
       "      <td>0.483677</td>\n",
       "      <td>0.283619</td>\n",
       "      <td>0.419193</td>\n",
       "      <td>0.173932</td>\n",
       "      <td>0.044847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9168</th>\n",
       "      <td>0.001038</td>\n",
       "      <td>0.196345</td>\n",
       "      <td>0.037914</td>\n",
       "      <td>0.243132</td>\n",
       "      <td>0.668358</td>\n",
       "      <td>0.471065</td>\n",
       "      <td>0.281836</td>\n",
       "      <td>0.413463</td>\n",
       "      <td>0.176833</td>\n",
       "      <td>0.049974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9169</th>\n",
       "      <td>0.000702</td>\n",
       "      <td>0.195334</td>\n",
       "      <td>0.037506</td>\n",
       "      <td>0.243990</td>\n",
       "      <td>0.193910</td>\n",
       "      <td>0.548204</td>\n",
       "      <td>0.247152</td>\n",
       "      <td>0.415666</td>\n",
       "      <td>0.204821</td>\n",
       "      <td>0.048237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9170</th>\n",
       "      <td>0.000458</td>\n",
       "      <td>0.172003</td>\n",
       "      <td>0.028110</td>\n",
       "      <td>0.248357</td>\n",
       "      <td>0.643583</td>\n",
       "      <td>0.397299</td>\n",
       "      <td>0.522955</td>\n",
       "      <td>0.932898</td>\n",
       "      <td>0.200590</td>\n",
       "      <td>0.033625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9171 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      component_1  component_2  component_3  component_4  component_5  \\\n",
       "0        0.000427     0.222139     0.041115     0.232067     0.186966   \n",
       "1        0.000611     0.221348     0.047978     0.239986     0.171936   \n",
       "2        0.000534     0.221348     0.047977     0.236577     0.191421   \n",
       "3        0.000473     0.221349     0.047977     0.236578     0.191433   \n",
       "4        0.000900     0.221344     0.047979     0.236575     0.191352   \n",
       "...           ...          ...          ...          ...          ...   \n",
       "9166     0.000839     0.184174     0.033012     0.247595     0.668313   \n",
       "9167     0.000321     0.206497     0.041994     0.237712     0.686587   \n",
       "9168     0.001038     0.196345     0.037914     0.243132     0.668358   \n",
       "9169     0.000702     0.195334     0.037506     0.243990     0.193910   \n",
       "9170     0.000458     0.172003     0.028110     0.248357     0.643583   \n",
       "\n",
       "      component_6  component_7  component_8  component_9  component_10  \n",
       "0        0.560488     0.248026     0.418244     0.206391      0.048862  \n",
       "1        0.588608     0.701865     0.147536     0.293047      0.095867  \n",
       "2        0.560389     0.246007     0.419400     0.205773      0.050282  \n",
       "3        0.560391     0.246014     0.419395     0.205770      0.050281  \n",
       "4        0.560374     0.245964     0.419429     0.205785      0.050290  \n",
       "...           ...          ...          ...          ...           ...  \n",
       "9166     0.501074     0.740327     0.143953     0.260548      0.088953  \n",
       "9167     0.483677     0.283619     0.419193     0.173932      0.044847  \n",
       "9168     0.471065     0.281836     0.413463     0.176833      0.049974  \n",
       "9169     0.548204     0.247152     0.415666     0.204821      0.048237  \n",
       "9170     0.397299     0.522955     0.932898     0.200590      0.033625  \n",
       "\n",
       "[9171 rows x 10 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "301cd55b-c732-4230-b1e6-8bd78f174cf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X_pca,y,test_size=0.33,random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f719b84-4828-4c43-b984-5db6a33e139d",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Model Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "dc0d137e-f314-4fcb-8ca4-0605552c65f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3223bb77-d85a-4dfb-b7a9-4fc8a677dce6",
   "metadata": {},
   "source": [
    "## Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "91db1388-c7d5-4daf-b700-2c17b7a28e13",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "tree = DecisionTreeClassifier(max_depth=3)\n",
    "clf = tree.fit(X_train,y_train)\n",
    "treepredict = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2e134a41-1592-4085-87db-f1ec5e3fd2ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7961678229269904"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(treepredict,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8356769b-a987-449c-a863-d20686bd031a",
   "metadata": {},
   "source": [
    "# Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c0d596b7-fd3a-40a6-8ce7-f370ab0ee217",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7429798480343575"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(max_depth=2,n_estimators=200)\n",
    "rclf = rf.fit(X_train,y_train)\n",
    "rfpred = rclf.predict(X_test)\n",
    "accuracy_score(rfpred,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c78bec66-5895-46d0-aab9-7d6fd865514a",
   "metadata": {},
   "source": [
    "# K-NN Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "43d7ce11-cde2-4159-9751-3165245378bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "neigh = KNeighborsClassifier(n_neighbors=3)\n",
    "knnclf = neigh.fit(X_train,y_train)\n",
    "y_pred = knnclf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "796b27fa-d2ad-4c26-9078-d7f8811e85e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8420878757846052"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_pred,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd8b3131-4848-44f2-80b2-1d797fa33de0",
   "metadata": {},
   "source": [
    "# SVM "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "dd7e9a76-1a2d-4dc0-ba83-c7e2a71b14c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7291047241493228"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svm = SVC(kernel=\"sigmoid\")\n",
    "sclf = svm.fit(X_train,y_train)\n",
    "y_pred = sclf.predict(X_test)\n",
    "accuracy_score(y_pred,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f72fcdd1-0238-4cad-9526-c194ebf72eca",
   "metadata": {},
   "source": [
    "# logisitic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3b4175aa-a2f6-4e7b-8c2f-27b333f9d229",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7502477700693756"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression(max_iter=1000)\n",
    "lrclf = lr.fit(X_train,y_train)\n",
    "y_pred = lrclf.predict(X_test)\n",
    "accuracy_score(y_pred,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ef5762b-f699-4130-b513-9b23cafe9916",
   "metadata": {},
   "source": [
    "# Now we can get clear with usage of PCA and now we will see without PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1beec383-e79c-4d58-8075-c4c410d8cdd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.218993</td>\n",
       "      <td>1.970629</td>\n",
       "      <td>0.976056</td>\n",
       "      <td>113.640746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9166</th>\n",
       "      <td>56</td>\n",
       "      <td>1.0</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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.218993</td>\n",
       "      <td>1.970629</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>77.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9167</th>\n",
       "      <td>22</td>\n",
       "      <td>1.0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.218993</td>\n",
       "      <td>1.970629</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9168</th>\n",
       "      <td>69</td>\n",
       "      <td>1.0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.218993</td>\n",
       "      <td>1.970629</td>\n",
       "      <td>1.270000</td>\n",
       "      <td>89.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9169</th>\n",
       "      <td>47</td>\n",
       "      <td>2.0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.218993</td>\n",
       "      <td>1.970629</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>88.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9170</th>\n",
       "      <td>31</td>\n",
       "      <td>1.0</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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.218993</td>\n",
       "      <td>1.970629</td>\n",
       "      <td>1.020000</td>\n",
       "      <td>65.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9171 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  sex  on_thyroxine  query_on_thyroxine  on_antithyroid_medication  \\\n",
       "0      29  2.0             0                   0                          0   \n",
       "1      41  2.0             0                   0                          0   \n",
       "2      36  2.0             0                   0                          0   \n",
       "3      32  2.0             0                   0                          0   \n",
       "4      60  2.0             0                   0                          0   \n",
       "...   ...  ...           ...                 ...                        ...   \n",
       "9166   56  1.0             0                   0                          0   \n",
       "9167   22  1.0             0                   0                          0   \n",
       "9168   69  1.0             0                   0                          0   \n",
       "9169   47  2.0             0                   0                          0   \n",
       "9170   31  1.0             0                   0                          0   \n",
       "\n",
       "      sick  pregnant  thyroid_surgery  I131_treatment  query_hypothyroid  \\\n",
       "0        0         0                0               0                  0   \n",
       "1        0         0                0               0                  0   \n",
       "2        0         0                0               0                  0   \n",
       "3        0         0                0               0                  0   \n",
       "4        0         0                0               0                  0   \n",
       "...    ...       ...              ...             ...                ...   \n",
       "9166     0         0                0               0                  0   \n",
       "9167     0         0                0               0                  0   \n",
       "9168     0         0                0               0                  0   \n",
       "9169     0         0                0               0                  0   \n",
       "9170     0         0                0               0                  1   \n",
       "\n",
       "      query_hyperthyroid  lithium  goitre  tumor  hypopituitary  psych  \\\n",
       "0                      0        0       0      0              0      0   \n",
       "1                      1        0       0      0              0      0   \n",
       "2                      0        0       0      0              0      0   \n",
       "3                      0        0       0      0              0      0   \n",
       "4                      0        0       0      0              0      0   \n",
       "...                  ...      ...     ...    ...            ...    ...   \n",
       "9166                   1        0       0      0              0      0   \n",
       "9167                   0        0       0      0              0      0   \n",
       "9168                   0        0       0      0              0      0   \n",
       "9169                   0        0       0      0              0      0   \n",
       "9170                   0        0       0      0              0      0   \n",
       "\n",
       "           TSH        T3       T4U         FTI  \n",
       "0     1.600000  1.900000  0.976056  113.640746  \n",
       "1     5.218993  1.970629  0.976056  113.640746  \n",
       "2     5.218993  1.970629  0.976056  113.640746  \n",
       "3     5.218993  1.970629  0.976056  113.640746  \n",
       "4     5.218993  1.970629  0.976056  113.640746  \n",
       "...        ...       ...       ...         ...  \n",
       "9166  5.218993  1.970629  0.830000   77.000000  \n",
       "9167  5.218993  1.970629  0.920000   99.000000  \n",
       "9168  5.218993  1.970629  1.270000   89.000000  \n",
       "9169  5.218993  1.970629  0.850000   88.000000  \n",
       "9170  5.218993  1.970629  1.020000   65.000000  \n",
       "\n",
       "[9171 rows x 20 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "01f20d3a-9c27-475e-bd67-08d85f577183",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "sns.heatmap(df2.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c2e85821-ee7d-4539-99c8-5e2918b5b4c2",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "age\n",
      "[   29    41    36    32    60    77    28    54    42    51    37    16\n",
      "    43    63    40    75    56    85    71    67    55    61    46    44\n",
      "    82    64    70    33    59    53    52    49    35    48    27    69\n",
      "    76    73    68    66    30    88    38    58    21    45    83    62\n",
      "    25    86    72    14    15    39    26    20    80    90    23    18\n",
      "    13    78    24    81    92    57    74     9    47    17    11    50\n",
      "    34     8    79    31    65    84    12    10    19    22     1     2\n",
      "    97     6    89    87   455    93     7    91     5    94     4 65511\n",
      "    95 65512     3 65526]\n",
      "\n",
      "\n",
      "\n",
      "sex\n",
      "[2. 1.]\n",
      "\n",
      "\n",
      "\n",
      "on_thyroxine\n",
      "[0 1]\n",
      "\n",
      "\n",
      "\n",
      "query_on_thyroxine\n",
      "[0 1]\n",
      "\n",
      "\n",
      "\n",
      "on_antithyroid_medication\n",
      "[0 1]\n",
      "\n",
      "\n",
      "\n",
      "sick\n",
      "[0 1]\n",
      "\n",
      "\n",
      "\n",
      "pregnant\n",
      "[0 1]\n",
      "\n",
      "\n",
      "\n",
      "thyroid_surgery\n",
      "[0 1]\n",
      "\n",
      "\n",
      "\n",
      "I131_treatment\n",
      "[0 1]\n",
      "\n",
      "\n",
      "\n",
      "query_hypothyroid\n",
      "[0 1]\n",
      "\n",
      "\n",
      "\n",
      "query_hyperthyroid\n",
      "[0 1]\n",
      "\n",
      "\n",
      "\n",
      "lithium\n",
      "[0 1]\n",
      "\n",
      "\n",
      "\n",
      "goitre\n",
      "[0 1]\n",
      "\n",
      "\n",
      "\n",
      "tumor\n",
      "[0 1]\n",
      "\n",
      "\n",
      "\n",
      "hypopituitary\n",
      "[0 1]\n",
      "\n",
      "\n",
      "\n",
      "psych\n",
      "[0 1]\n",
      "\n",
      "\n",
      "\n",
      "TSH\n",
      "[1.60000000e+00 5.21899327e+00 7.00000000e-01 1.20000000e+00\n",
      " 1.90000000e+00 1.00000000e+00 5.00000000e-01 2.60000000e+00\n",
      " 6.80000000e+01 1.50000000e+00 5.90000000e+00 5.00000000e-02\n",
      " 4.00000000e+00 4.00000000e-01 8.00000000e-01 2.00000000e-01\n",
      " 3.00000000e+00 3.00000000e-01 9.59999900e+00 1.40000000e+02\n",
      " 6.00000000e-01 1.70000000e+00 2.50000000e+00 6.80000000e+00\n",
      " 2.10000000e+00 1.10000000e+00 9.79999900e+00 3.70000000e+00\n",
      " 1.00000000e-01 3.50000000e-01 9.00000000e+01 1.30000000e+00\n",
      " 2.70000000e+00 2.90000000e+00 2.50000000e-01 5.80000000e+00\n",
      " 2.00000000e+00 9.00000000e-01 3.20000000e+00 7.40000000e+00\n",
      " 1.80000000e+00 5.00000000e+00 7.00000000e+01 6.50000000e+00\n",
      " 1.15999990e+01 4.20000000e+00 8.80000000e+01 2.20000000e+00\n",
      " 5.30000000e+01 1.65000000e+01 8.40000000e+00 4.10000000e+00\n",
      " 3.30000000e+00 8.00000000e+01 1.76000000e+02 3.00000000e+01\n",
      " 1.10000000e+01 6.00000000e+00 7.10000000e+01 9.09999900e+00\n",
      " 1.07000000e+01 4.90000000e+00 3.40000000e+00 1.83000000e+00\n",
      " 4.60000000e+00 1.40000000e+00 1.40000000e+01 3.50000000e+00\n",
      " 4.60000000e+01 1.80000000e+01 1.70000000e+02 1.91999990e+01\n",
      " 7.50000000e+00 1.45000000e+02 7.90000000e+00 1.90000000e+01\n",
      " 4.80000000e+01 2.50000000e+01 7.30000000e+00 6.70000000e+00\n",
      " 1.53000000e+02 3.50000000e+01 3.80000000e+00 2.30000000e+00\n",
      " 9.20000000e+00 1.20000000e+01 5.50000000e+00 1.52999990e+01\n",
      " 1.50000000e-01 1.02999990e+01 4.30000000e+02 2.13000000e+02\n",
      " 3.70000000e+01 2.16000000e+02 2.60000000e+01 4.30000000e+00\n",
      " 2.40000000e+00 4.70000000e+01 8.20000000e+00 1.44000000e+01\n",
      " 1.74000000e+01 1.00000000e+02 1.32000000e+01 4.10000000e+01\n",
      " 3.10000000e+00 2.20000000e+01 4.50000000e+01 4.50000000e+00\n",
      " 2.40000000e+01 6.50000000e+01 4.40000000e+00 5.60000000e+01\n",
      " 1.14000000e+01 5.20000000e+00 5.60000000e+00 8.29999900e+00\n",
      " 4.00000000e+02 2.60000000e+02 5.40000000e+00 8.00000000e+00\n",
      " 6.60000000e+00 8.50000000e+00 5.10000000e+00 1.38000000e+02\n",
      " 4.20000000e+01 4.40000000e+01 2.80000000e+00 1.00000000e+01\n",
      " 5.40000000e+01 3.90000000e+00 1.50000000e+02 3.10000000e+01\n",
      " 6.50000000e-01 2.00000000e+02 8.50000000e+01 8.60000000e+01\n",
      " 6.30000000e+00 5.00000000e+01 3.40000000e+01 1.60000000e+01\n",
      " 9.40000000e+00 1.42999990e+01 4.90000000e+01 6.20000000e+00\n",
      " 8.20000000e+01 7.70000000e+01 9.20000000e+01 1.30000000e+01\n",
      " 4.70000000e+00 5.20000000e+01 2.70000000e+01 8.79999900e+00\n",
      " 1.05000000e+00 8.09999900e+00 1.32999990e+01 2.80000000e+01\n",
      " 1.25000000e+02 1.50000000e+01 1.01000000e+00 3.90000000e+01\n",
      " 2.88000000e+02 2.10000000e+01 3.60000000e+00 4.50000000e-01\n",
      " 1.43000000e+02 2.35000000e+02 5.50000000e+01 7.60000000e+00\n",
      " 3.80000000e+01 3.20000000e+01 6.10000000e+01 4.80000000e+00\n",
      " 1.47999990e+01 7.00000000e+00 2.00000000e+01 9.00000000e+00\n",
      " 9.80000000e+01 2.90000000e+01 6.40000000e+00 1.70000000e+01\n",
      " 2.30000000e+01 1.84000000e+01 3.60000000e+01 1.09000000e+02\n",
      " 8.59999900e+00 6.10000000e+00 1.26000000e+02 4.30000000e+01\n",
      " 9.70000000e+00 4.00000000e-02 1.20000000e-01 6.90000000e+00\n",
      " 1.60000000e+02 1.60000000e-01 5.30000000e-01 8.80000000e-01\n",
      " 2.60000000e-01 2.50000000e-02 3.90000000e-01 7.70000000e-01\n",
      " 7.00000000e-02 4.00000000e+01 2.30000000e-01 8.40000000e-01\n",
      " 9.50000000e-01 3.80000000e-01 4.10000000e-01 6.50000000e-02\n",
      " 5.00000000e-03 7.80000000e-01 8.10000000e-01 1.83000000e+02\n",
      " 6.10000000e-01 3.20000000e-01 8.20000000e-01 8.90000000e+00\n",
      " 8.70000000e-01 9.80000000e-01 4.70000000e-01 5.80000000e-01\n",
      " 4.30000000e-01 8.00000000e-02 8.60000000e-01 8.90000000e-01\n",
      " 9.10000000e-01 5.30000000e+02 1.00000000e-02 5.20000000e-01\n",
      " 8.30000000e-01 1.50000000e-02 3.00000000e-02 5.90000000e-01\n",
      " 6.20000000e-01 7.40000000e-01 4.60000000e-01 1.78000000e+02\n",
      " 2.00000000e-02 9.00000000e-02 1.65000000e+02 6.80000000e-01\n",
      " 8.50000000e-01 3.50000000e-02 1.70000000e-01 7.10000000e+00\n",
      " 7.80000000e+00 9.90000000e+00 4.20000000e-01 2.80000000e-01\n",
      " 3.70000000e-01 4.40000000e+02 2.70000000e-01 6.30000000e-01\n",
      " 7.10000000e-01 5.70000000e-01 9.20000000e-01 7.20000000e-01\n",
      " 4.90000000e-01 9.40000000e-01 9.70000000e-01 5.60000000e-01\n",
      " 5.40000000e-01 7.90000000e-01 1.03000000e+02 7.50000000e-01\n",
      " 5.73000000e+00 6.70000000e-01 7.20000000e+00 1.10999990e+01\n",
      " 9.30000000e-01 5.10000000e-01 9.50000000e+00 7.60000000e-01\n",
      " 7.70000000e+00 6.00000000e-02 6.40000000e-01 5.70000000e+00\n",
      " 7.30000000e-01 1.99000000e+02 9.90000000e+01 5.80000000e+01\n",
      " 2.20000000e-01 6.60000000e+01 9.60000000e-01 1.90000000e-01\n",
      " 2.40000000e-01 5.50000000e-01 1.80000000e-01 2.10000000e-01\n",
      " 4.80000000e-01 1.02000000e+00 4.40000000e-01 9.90000000e-01\n",
      " 3.40000000e-01 3.30000000e-01 1.17000000e+02 6.90000000e-01\n",
      " 8.90000000e+01 7.60000000e+01 1.51000000e+02 5.10000000e+01\n",
      " 1.39000000e+02 1.40000000e-01 2.90000000e-01 7.80000000e+01\n",
      " 4.72000000e+02 5.30000000e+00 2.30000000e+02 3.10000000e-01\n",
      " 1.20999990e+01 4.50000000e-02 5.50000000e-02 6.00000000e+01\n",
      " 1.30000000e-01 1.08000000e+02 1.88000000e+02 3.05000000e+01\n",
      " 4.68000000e+02 3.60000000e-01 2.64000000e+01 6.60000000e-01\n",
      " 2.36000000e+02 3.30000000e+01 4.78000000e+02 9.29999900e+00\n",
      " 1.16000000e+02 2.55000000e-01 1.10000000e-01 1.14000000e+02\n",
      " 1.03000000e+00 1.97000000e+02 9.60000000e+01 1.15000000e+01\n",
      " 1.31000000e+02 6.70000000e+01 1.06000000e+02 1.72000000e+02\n",
      " 1.64000000e+02 7.50000000e-02 1.41000000e+02 1.04000000e+00\n",
      " 1.35000000e-01 8.50000000e-02 1.25000000e-01 1.15000000e-01\n",
      " 7.30000000e+01 4.36000000e+02 9.50000000e-02 1.19000000e+01\n",
      " 4.94000000e+02 6.20000000e+01 3.93000000e+02 8.70000000e+00\n",
      " 1.05000000e-01 2.52000000e+02 5.90000000e+01 1.18000000e+02\n",
      " 1.14000000e+00 1.12000000e+00 1.91000000e+02 1.66000000e+02\n",
      " 4.60000000e+02 1.92000000e+02 1.36000000e+02 1.77000000e+02\n",
      " 1.98000000e+02 1.37000000e+01 1.67999990e+01 7.40000000e+01\n",
      " 5.00000000e+02 4.01000000e+00]\n",
      "\n",
      "\n",
      "\n",
      "T3\n",
      "[ 1.9         1.97062881  2.6         1.8         1.7         2.3\n",
      "  2.4         2.9         2.          2.1         1.6         0.1\n",
      "  1.4         1.2         1.5         1.3         2.5         2.7\n",
      "  2.2         2.8         3.2         0.4         0.8         1.\n",
      "  1.1         3.7         4.4         3.          3.1         3.6\n",
      "  7.6         0.9         4.2         0.5         0.6         0.3\n",
      "  0.7         3.8         0.2         4.1         6.6         4.7\n",
      "  8.599999    3.3         4.3         0.05        3.4         4.6\n",
      "  4.9         6.2         3.5         3.9         8.9         4.5\n",
      "  8.099999    5.          4.8         5.1         5.3         6.7\n",
      "  7.3         6.1         4.          5.5         5.4         5.7\n",
      "  7.          6.          7.1         8.5        10.599999    1.44\n",
      "  5.2         6.9         6.8         5.6         6.4        18.\n",
      "  9.5         6.5        13.299999    0.83        0.69        0.93\n",
      "  5.9         8.        ]\n",
      "\n",
      "\n",
      "\n",
      "T4U\n",
      "[0.97605572 1.02       1.06       0.94       1.08       0.84\n",
      " 1.13       1.07       0.87       0.89       0.62       0.91\n",
      " 0.68       1.         1.38       0.79       0.95       1.57\n",
      " 0.92       1.48       1.1        0.7        1.01       1.05\n",
      " 0.96       0.78       1.4        0.66       0.86       0.76\n",
      " 0.9        1.16       1.12       0.98       1.04       1.26\n",
      " 0.83       0.97       0.93       0.88       0.73       1.29\n",
      " 1.3        0.75       0.8        1.83       1.03       0.61\n",
      " 1.44       1.18       0.59       0.81       0.64       1.2\n",
      " 0.82       1.19       0.99       1.56       1.22       0.71\n",
      " 1.32       0.67       0.32       1.11       0.85       0.52\n",
      " 1.15       1.21       0.77       0.69       1.51       1.33\n",
      " 0.55       1.45       1.24       1.79       0.72       1.73\n",
      " 1.27       1.68       1.09       1.43       0.35       0.3\n",
      " 1.28       0.2        1.41       1.14       0.53       1.52\n",
      " 1.23       0.74       1.53       1.62       1.66       0.4\n",
      " 1.86       1.59       0.29       0.34       1.17       1.76\n",
      " 0.57       0.63       1.71       0.31       0.49       1.31\n",
      " 1.34       0.5        1.75       1.36       0.36       1.42\n",
      " 0.6        1.74       1.46       1.63       0.28       1.47\n",
      " 1.25       1.96       1.39       0.48       0.56       1.55\n",
      " 0.65       1.69       1.64       1.65       1.67       1.77\n",
      " 1.82       0.19       1.49       1.35       1.5        1.97\n",
      " 1.94       1.58       0.58       1.61       0.54       1.7\n",
      " 0.38       0.41       0.25       2.03       1.93       0.944\n",
      " 1.8        2.12       2.01       1.54       1.88       0.46\n",
      " 1.84       1.37       0.47       2.32       1.78       1.89\n",
      " 0.44       0.17       1.6        1.87       0.37       0.45\n",
      " 1.95       0.51       2.33       0.42       1.81       2.15\n",
      " 2.02       2.16       2.        ]\n",
      "\n",
      "\n",
      "\n",
      "FTI\n",
      "[113.64074552  47.          85.          84.          96.\n",
      " 105.          95.         106.         176.         129.\n",
      " 100.          69.          39.          91.          90.\n",
      "  93.          66.         121.          92.         173.\n",
      " 117.          31.         113.          67.         101.\n",
      " 126.         123.         149.          68.          86.\n",
      " 132.         131.         116.          97.         124.\n",
      " 136.         142.         104.           7.5        107.\n",
      "  73.         110.         130.          88.         128.\n",
      " 122.         102.         134.         163.          63.\n",
      " 354.          81.         109.         114.         133.\n",
      " 170.          99.         111.         108.         161.\n",
      "  78.         148.          98.         135.          80.\n",
      " 127.         213.         119.          65.          89.\n",
      " 143.         316.         155.         172.         150.\n",
      " 103.         120.         258.           5.         272.\n",
      " 263.         166.         138.          52.         164.\n",
      " 337.          94.         118.         182.          41.\n",
      "  70.         144.          10.           4.          13.\n",
      "  87.         140.          74.         152.          77.\n",
      "   3.          82.         145.          64.          79.\n",
      " 147.          54.          83.         634.         650.\n",
      "  12.          61.          11.         115.          35.\n",
      "  17.         165.         167.         153.          44.\n",
      "   3.4         55.          71.         253.          75.\n",
      "   2.5        197.          24.         156.         237.\n",
      " 203.         112.         141.           3.5        190.\n",
      "  37.          45.         193.          57.          76.\n",
      " 160.           6.         200.         485.          49.\n",
      " 158.         137.         428.         450.         174.\n",
      " 189.         202.         159.         196.         154.\n",
      " 139.          34.         222.         184.         178.\n",
      " 146.         125.          21.         157.          51.\n",
      " 839.         332.         151.         305.         299.\n",
      " 266.          32.          53.         370.          22.\n",
      " 168.          60.         187.         171.         220.\n",
      " 169.         232.         254.         345.         194.\n",
      " 211.         217.         550.          23.         257.\n",
      " 188.         192.         179.         218.         208.\n",
      "   6.6        240.          43.         259.         265.\n",
      "   9.         212.         347.         216.          29.\n",
      " 186.         162.         256.         177.          20.\n",
      "  33.         308.         482.          16.         180.\n",
      "  59.         264.         881.         204.          15.\n",
      "   1.4        271.          58.         334.         214.\n",
      " 228.         205.          72.         183.         612.\n",
      " 445.         244.         175.           2.8        283.\n",
      "  62.           7.6          5.4        199.         209.\n",
      " 227.          26.          48.         221.          28.\n",
      " 195.         395.         206.          46.         198.\n",
      " 215.           8.5         56.          14.         223.\n",
      "   8.9          8.4         18.           9.099999    50.\n",
      " 207.         185.         291.         235.          36.\n",
      " 224.          19.           7.         312.         247.\n",
      "  40.         274.         210.         242.         251.\n",
      "  27.         181.           2.         249.          42.\n",
      " 349.         191.         280.         281.         201.\n",
      " 245.         362.         219.         239.         273.\n",
      " 356.           2.4          8.299999   236.           8.7\n",
      " 262.           3.84       231.         369.           3.1\n",
      " 378.         546.         268.          25.         298.\n",
      " 241.         642.         238.         233.         325.\n",
      " 255.           4.5        519.           4.15       297.\n",
      "   4.85       321.         226.         290.         243.\n",
      "   5.5        288.          38.         329.        ]\n"
     ]
    }
   ],
   "source": [
    "# there is no so much correlation\n",
    "# now we can normalize the value\n",
    "for i in df2.columns:\n",
    "    print(\"\\n\\n\")\n",
    "    print(i)\n",
    "    print(df2[i].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8a3a002f-085e-43c8-bfbb-b653e6c6ce68",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['age','sex','TSH','T3','T4U','FTI']\n",
    "for i in cols:\n",
    "    df2[i] = scaler.fit_transform(df2[[i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3930491a-5dd4-4b71-affb-bdb67d7e8bf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting X and y values\n",
    "\n",
    "X = df2\n",
    "y = df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "26efe75b-cb5f-4b5a-81a6-fbeea29ffa9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.33,random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed78aae5-545d-445a-affa-fd93c9cbc5a8",
   "metadata": {},
   "source": [
    "# Model Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "3ae8d3db-7f4b-46c4-9f00-af382f4c0341",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f737753-122d-4374-a9bb-a230f189a12d",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "134921a5-3224-4791-82e9-0903f8eaab9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8457218368021143"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "tree = DecisionTreeClassifier(max_depth=3)\n",
    "clf = tree.fit(X_train,y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "accuracy_score(y_pred,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52ab3f36-6a85-4f59-92d9-9f7f970a955b",
   "metadata": {},
   "source": [
    "# Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e6a7e74a-643d-4be8-9499-ee9ac0c1db72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7403369672943508"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(max_depth=2,n_estimators=200)\n",
    "rclf = rf.fit(X_train,y_train)\n",
    "y_pred = rclf.predict(X_test)\n",
    "accuracy_score(y_pred,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b0dc0ca-6486-46e7-b475-44741ed47b15",
   "metadata": {},
   "source": [
    "# K-NN Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5ac46398-a09b-46a7-9e31-7f9921ec4577",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8186323092170465"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "neigh = KNeighborsClassifier(n_neighbors=3)\n",
    "knnclf = neigh.fit(X_train,y_train)\n",
    "y_pred = knnclf.predict(X_test)\n",
    "accuracy_score(y_pred,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b55446af-bb43-452d-97eb-412c3e6de436",
   "metadata": {},
   "source": [
    "# SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "735b19e1-f4d6-4005-a904-1130d4b34220",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7456227287743641"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svm = SVC(kernel=\"sigmoid\")\n",
    "sclf = svm.fit(X_train,y_train)\n",
    "y_pred = sclf.predict(X_test)\n",
    "accuracy_score(y_pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f92f7656-2371-4ee2-a9bd-1e4bc28ad2f7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e8051f57-da33-469a-9c2c-976f37306e5a",
   "metadata": {},
   "source": [
    "# Now we can say that with PCA technique we can use KNN because it provides best result as 84% but without PCA with lot of varaible we can use decision tree to get better result you can get some insights by above methods\n",
    "\n",
    "\n",
    "# Thanks INEURON for wonderful dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5cde49e4-650b-42ff-a681-bad2640dbeab",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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